feat: ✨ YOLO-World-Seg files uploaded

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>

LICENSE

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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
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+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
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+  The licenses for most software and other practical works are designed
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+
+                       TERMS AND CONDITIONS
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+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
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+
+  13. Use with the GNU Affero General Public License.
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+
+  14. Revised Versions of this License.
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+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
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+  If the Program specifies that a proxy can decide which future
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+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
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+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

app.py

@@ -1,7 +1,16 @@
+import os
+os.system("mim install 'mmengine>=0.7.0'")
+os.system("mim install mmcv")
+os.system("mim install 'mmdet>=3.0.0'")
+os.system("pip install -e .")
+
+
+from yolo_world import version
+
 import gradio as gr
 
 def greet(name):
-    return "Test " + name + "!!"
+    return version
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 iface.launch()

configs/finetune_coco/yolo_world_l_dual_vlpan_2e-4_80e_8gpus_finetune_coco.py

@@ -0,0 +1,184 @@
+_base_ = (
+    '../../third_party/mmyolo/configs/yolov8/'
+    'yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py')
+custom_imports = dict(
+    imports=['yolo_world'],
+    allow_failed_imports=False)
+
+# hyper-parameters
+num_classes = 80
+num_training_classes = 80
+max_epochs = 80  # Maximum training epochs
+close_mosaic_epochs = 10
+save_epoch_intervals = 5
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-4
+weight_decay = 0.05
+train_batch_size_per_gpu = 16
+load_from='pretrained_models/yolo_world_l_clip_base_dual_vlpan_2e-3adamw_32xb16_100e_o365_goldg_train_pretrained-0e566235.pth'
+persistent_workers = False
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=['all'])),
+    neck=dict(type='YOLOWolrdDualPAFPN',
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'),
+              text_enhancder=dict(type='ImagePoolingAttentionModule',
+                                  embed_channels=256,
+                                  num_heads=8)),
+    bbox_head=dict(type='YOLOWorldHead',
+                   head_module=dict(type='YOLOWorldHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes)),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)))
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+mosaic_affine_transform = [
+    dict(
+        type='MultiModalMosaic',
+        img_scale=_base_.img_scale,
+        pad_val=114.0,
+        pre_transform=_base_.pre_transform),
+    dict(type='YOLOv5CopyPaste', prob=_base_.copypaste_prob),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        max_aspect_ratio=100.,
+        scaling_ratio_range=(1 - _base_.affine_scale,
+                             1 + _base_.affine_scale),
+        # img_scale is (width, height)
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        min_area_ratio=_base_.min_area_ratio,
+        use_mask_refine=_base_.use_mask2refine)
+]
+train_pipeline = [
+    *_base_.pre_transform,
+    *mosaic_affine_transform,
+    dict(
+        type='YOLOv5MultiModalMixUp',
+        prob=_base_.mixup_prob,
+        pre_transform=[*_base_.pre_transform,
+                       *mosaic_affine_transform]),
+    *_base_.last_transform[:-1],
+    *text_transform
+]
+train_pipeline_stage2 = [
+    *_base_.train_pipeline_stage2[:-1],
+    *text_transform
+]
+coco_train_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(
+        type='YOLOv5CocoDataset',
+        data_root='data/coco',
+        ann_file='annotations/instances_train2017.json',
+        data_prefix=dict(img='train2017/'),
+        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
+    class_text_path='data/texts/coco_class_texts.json',
+    pipeline=train_pipeline)
+
+train_dataloader = dict(
+    persistent_workers=persistent_workers,
+    batch_size=train_batch_size_per_gpu,
+    collate_fn=dict(type='yolow_collate'),
+    dataset=coco_train_dataset)
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'pad_param', 'texts'))
+]
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(
+        type='YOLOv5CocoDataset',
+        data_root='data/coco',
+        ann_file='annotations/instances_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
+    class_text_path='data/texts/coco_class_texts.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+# training settings
+default_hooks = dict(
+    param_scheduler=dict(
+        scheduler_type='linear',
+        lr_factor=0.01,
+        max_epochs=max_epochs),
+    checkpoint=dict(
+        max_keep_ckpts=-1,
+        save_best=None,
+        interval=save_epoch_intervals))
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0001,
+        update_buffers=True,
+        strict_load=False,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - close_mosaic_epochs,
+        switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(
+    max_epochs=max_epochs,
+    val_interval=5,
+    dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                        _base_.val_interval_stage2)])
+optim_wrapper = dict(
+    optimizer=dict(
+        _delete_=True,
+        type='AdamW',
+        lr=base_lr,
+        weight_decay=weight_decay,
+        batch_size_per_gpu=train_batch_size_per_gpu),
+    paramwise_cfg=dict(
+        bias_decay_mult=0.0,
+        norm_decay_mult=0.0,
+        custom_keys={'backbone.text_model': dict(lr_mult=0.01),
+                     'logit_scale': dict(weight_decay=0.0)}),
+    constructor='YOLOWv5OptimizerConstructor')
+
+# evaluation settings
+val_evaluator = dict(
+    _delete_=True,
+    type='mmdet.CocoMetric',
+    proposal_nums=(100, 1, 10),
+    ann_file='data/coco/annotations/instances_val2017.json',
+    metric='bbox')

configs/finetune_coco/yolo_world_l_efficient_neck_2e-4_80e_8gpus_finetune_coco.py

@@ -0,0 +1,161 @@
+_base_ = ('../../third_party/mmyolo/configs/yolov8/'
+          'yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py')
+custom_imports = dict(imports=['yolo_world'], allow_failed_imports=False)
+
+# hyper-parameters
+num_classes = 80
+num_training_classes = 80
+max_epochs = 80  # Maximum training epochs
+close_mosaic_epochs = 10
+save_epoch_intervals = 5
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-4
+weight_decay = 0.05
+train_batch_size_per_gpu = 16
+load_from = 'pretrained_models/yolo_world_l_clip_base_dual_vlpan_2e-3adamw_32xb16_100e_o365_goldg_train_pretrained-0e566235.pth'
+persistent_workers = False
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=['all'])),
+    neck=dict(type='YOLOWorldPAFPN',
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='EfficientCSPLayerWithTwoConv')),
+    bbox_head=dict(type='YOLOWorldHead',
+                   head_module=dict(type='YOLOWorldHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes)),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)))
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+mosaic_affine_transform = [
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=_base_.pre_transform),
+    dict(type='YOLOv5CopyPaste', prob=_base_.copypaste_prob),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        max_aspect_ratio=100.,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        # img_scale is (width, height)
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        min_area_ratio=_base_.min_area_ratio,
+        use_mask_refine=_base_.use_mask2refine)
+]
+train_pipeline = [
+    *_base_.pre_transform, *mosaic_affine_transform,
+    dict(type='YOLOv5MultiModalMixUp',
+         prob=_base_.mixup_prob,
+         pre_transform=[*_base_.pre_transform, *mosaic_affine_transform]),
+    *_base_.last_transform[:-1], *text_transform
+]
+train_pipeline_stage2 = [*_base_.train_pipeline_stage2[:-1], *text_transform]
+coco_train_dataset = dict(_delete_=True,
+                          type='MultiModalDataset',
+                          dataset=dict(
+                              type='YOLOv5CocoDataset',
+                              data_root='data/coco',
+                              ann_file='annotations/instances_train2017.json',
+                              data_prefix=dict(img='train2017/'),
+                              filter_cfg=dict(filter_empty_gt=False,
+                                              min_size=32)),
+                          class_text_path='data/texts/coco_class_texts.json',
+                          pipeline=train_pipeline)
+
+train_dataloader = dict(persistent_workers=persistent_workers,
+                        batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=coco_train_dataset)
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5CocoDataset',
+                 data_root='data/coco',
+                 ann_file='annotations/instances_val2017.json',
+                 data_prefix=dict(img='val2017/'),
+                 filter_cfg=dict(filter_empty_gt=False, min_size=32)),
+    class_text_path='data/texts/coco_class_texts.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+# training settings
+default_hooks = dict(param_scheduler=dict(scheduler_type='linear',
+                                          lr_factor=0.01,
+                                          max_epochs=max_epochs),
+                     checkpoint=dict(max_keep_ckpts=-1,
+                                     save_best=None,
+                                     interval=save_epoch_intervals))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=5,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0)
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')
+
+# evaluation settings
+val_evaluator = dict(_delete_=True,
+                     type='mmdet.CocoMetric',
+                     proposal_nums=(100, 1, 10),
+                     ann_file='data/coco/annotations/instances_val2017.json',
+                     metric='bbox')

configs/pretrain/yolo_world_l_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py

@@ -0,0 +1,172 @@
+_base_ = ('../../third_party/mmyolo/configs/yolov8/'
+          'yolov8_l_syncbn_fast_8xb16-500e_coco.py')
+custom_imports = dict(imports=['yolo_world'],
+                      allow_failed_imports=False)
+
+# hyper-parameters
+num_classes = 1203
+num_training_classes = 80
+max_epochs = 100  # Maximum training epochs
+close_mosaic_epochs = 2
+save_epoch_intervals = 2
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-3
+weight_decay = 0.05 / 2
+train_batch_size_per_gpu = 16
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=['all'])),
+    neck=dict(type='YOLOWolrdDualPAFPN',
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'),
+              text_enhancder=dict(type='ImagePoolingAttentionModule',
+                                  embed_channels=256,
+                                  num_heads=8)),
+    bbox_head=dict(type='YOLOWorldHead',
+                   head_module=dict(type='YOLOWorldHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes)),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)))
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+train_pipeline = [
+    *_base_.pre_transform,
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=_base_.pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        max_aspect_ratio=_base_.max_aspect_ratio,
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114)),
+    *_base_.last_transform[:-1],
+    *text_transform,
+]
+train_pipeline_stage2 = [*_base_.train_pipeline_stage2[:-1], *text_transform]
+obj365v1_train_dataset = dict(
+    type='MultiModalDataset',
+    dataset=dict(
+        type='YOLOv5Objects365V1Dataset',
+        data_root='data/objects365v1/',
+        ann_file='annotations/objects365_train.json',
+        data_prefix=dict(img='train/'),
+        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
+    class_text_path='data/texts/obj365v1_class_texts.json',
+    pipeline=train_pipeline)
+
+mg_train_dataset = dict(type='YOLOv5MixedGroundingDataset',
+                        data_root='data/mixed_grounding/',
+                        ann_file='annotations/final_mixed_train_no_coco.json',
+                        data_prefix=dict(img='gqa/images/'),
+                        filter_cfg=dict(filter_empty_gt=False, min_size=32),
+                        pipeline=train_pipeline)
+
+flickr_train_dataset = dict(
+    type='YOLOv5MixedGroundingDataset',
+    data_root='data/flickr/',
+    ann_file='annotations/final_flickr_separateGT_train.json',
+    data_prefix=dict(img='full_images/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
+    pipeline=train_pipeline)
+
+train_dataloader = dict(batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=dict(_delete_=True,
+                                     type='ConcatDataset',
+                                     datasets=[
+                                         obj365v1_train_dataset,
+                                         flickr_train_dataset, mg_train_dataset
+                                     ],
+                                     ignore_keys=['classes', 'palette']))
+
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco/',
+                 test_mode=True,
+                 ann_file='lvis/lvis_v1_minival_inserted_image_name.json',
+                 data_prefix=dict(img=''),
+                 batch_shapes_cfg=None),
+    class_text_path='data/texts/lvis_v1_class_texts.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='mmdet.LVISMetric',
+                     ann_file='data/coco/lvis/lvis_v1_minival_inserted_image_name.json',
+                     metric='bbox')
+test_evaluator = val_evaluator
+
+# training settings
+default_hooks = dict(param_scheduler=dict(max_epochs=max_epochs),
+                     checkpoint=dict(interval=save_epoch_intervals,
+                                     rule='greater'))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=10,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0)
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')

configs/pretrain/yolo_world_l_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_val.py

@@ -0,0 +1,172 @@
+_base_ = ('../../third_party/mmyolo/configs/yolov8/'
+          'yolov8_l_syncbn_fast_8xb16-500e_coco.py')
+custom_imports = dict(imports=['yolo_world'],
+                      allow_failed_imports=False)
+
+# hyper-parameters
+num_classes = 1203
+num_training_classes = 80
+max_epochs = 100  # Maximum training epochs
+close_mosaic_epochs = 2
+save_epoch_intervals = 2
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-3
+weight_decay = 0.05 / 2
+train_batch_size_per_gpu = 16
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=['all'])),
+    neck=dict(type='YOLOWolrdDualPAFPN',
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'),
+              text_enhancder=dict(type='ImagePoolingAttentionModule',
+                                  embed_channels=256,
+                                  num_heads=8)),
+    bbox_head=dict(type='YOLOWorldHead',
+                   head_module=dict(type='YOLOWorldHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes)),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)))
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+train_pipeline = [
+    *_base_.pre_transform,
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=_base_.pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        max_aspect_ratio=_base_.max_aspect_ratio,
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114)),
+    *_base_.last_transform[:-1],
+    *text_transform,
+]
+train_pipeline_stage2 = [*_base_.train_pipeline_stage2[:-1], *text_transform]
+obj365v1_train_dataset = dict(
+    type='MultiModalDataset',
+    dataset=dict(
+        type='YOLOv5Objects365V1Dataset',
+        data_root='data/objects365v1/',
+        ann_file='annotations/objects365_train.json',
+        data_prefix=dict(img='train/'),
+        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
+    class_text_path='data/texts/obj365v1_class_texts.json',
+    pipeline=train_pipeline)
+
+mg_train_dataset = dict(type='YOLOv5MixedGroundingDataset',
+                        data_root='data/mixed_grounding/',
+                        ann_file='annotations/final_mixed_train_no_coco.json',
+                        data_prefix=dict(img='gqa/images/'),
+                        filter_cfg=dict(filter_empty_gt=False, min_size=32),
+                        pipeline=train_pipeline)
+
+flickr_train_dataset = dict(
+    type='YOLOv5MixedGroundingDataset',
+    data_root='data/flickr/',
+    ann_file='annotations/final_flickr_separateGT_train.json',
+    data_prefix=dict(img='full_images/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
+    pipeline=train_pipeline)
+
+train_dataloader = dict(batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=dict(_delete_=True,
+                                     type='ConcatDataset',
+                                     datasets=[
+                                         obj365v1_train_dataset,
+                                         flickr_train_dataset, mg_train_dataset
+                                     ],
+                                     ignore_keys=['classes', 'palette']))
+
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco/',
+                 test_mode=True,
+                 ann_file='lvis/lvis_v1_val.json',
+                 data_prefix=dict(img=''),
+                 batch_shapes_cfg=None),
+    class_text_path='data/texts/lvis_v1_class_texts.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='mmdet.LVISMetric',
+                     ann_file='data/coco/lvis/lvis_v1_val.json',
+                     metric='bbox')
+test_evaluator = val_evaluator
+
+# training settings
+default_hooks = dict(param_scheduler=dict(max_epochs=max_epochs),
+                     checkpoint=dict(interval=save_epoch_intervals,
+                                     rule='greater'))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=10,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0)
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')

configs/pretrain/yolo_world_m_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py

@@ -0,0 +1,172 @@
+_base_ = ('../../third_party/mmyolo/configs/yolov8/'
+          'yolov8_m_syncbn_fast_8xb16-500e_coco.py')
+custom_imports = dict(imports=['yolo_world'],
+                      allow_failed_imports=False)
+
+# hyper-parameters
+num_classes = 1203
+num_training_classes = 80
+max_epochs = 100  # Maximum training epochs
+close_mosaic_epochs = 2
+save_epoch_intervals = 2
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-3
+weight_decay = 0.05 / 2
+train_batch_size_per_gpu = 16
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=['all'])),
+    neck=dict(type='YOLOWolrdDualPAFPN',
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'),
+              text_enhancder=dict(type='ImagePoolingAttentionModule',
+                                  embed_channels=256,
+                                  num_heads=8)),
+    bbox_head=dict(type='YOLOWorldHead',
+                   head_module=dict(type='YOLOWorldHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes)),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)))
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+train_pipeline = [
+    *_base_.pre_transform,
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=_base_.pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        max_aspect_ratio=_base_.max_aspect_ratio,
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114)),
+    *_base_.last_transform[:-1],
+    *text_transform,
+]
+train_pipeline_stage2 = [*_base_.train_pipeline_stage2[:-1], *text_transform]
+obj365v1_train_dataset = dict(
+    type='MultiModalDataset',
+    dataset=dict(
+        type='YOLOv5Objects365V1Dataset',
+        data_root='data/objects365v1/',
+        ann_file='annotations/objects365_train.json',
+        data_prefix=dict(img='train/'),
+        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
+    class_text_path='data/texts/obj365v1_class_texts.json',
+    pipeline=train_pipeline)
+
+mg_train_dataset = dict(type='YOLOv5MixedGroundingDataset',
+                        data_root='data/mixed_grounding/',
+                        ann_file='annotations/final_mixed_train_no_coco.json',
+                        data_prefix=dict(img='gqa/images/'),
+                        filter_cfg=dict(filter_empty_gt=False, min_size=32),
+                        pipeline=train_pipeline)
+
+flickr_train_dataset = dict(
+    type='YOLOv5MixedGroundingDataset',
+    data_root='data/flickr/',
+    ann_file='annotations/final_flickr_separateGT_train.json',
+    data_prefix=dict(img='full_images/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
+    pipeline=train_pipeline)
+
+train_dataloader = dict(batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=dict(_delete_=True,
+                                     type='ConcatDataset',
+                                     datasets=[
+                                         obj365v1_train_dataset,
+                                         flickr_train_dataset, mg_train_dataset
+                                     ],
+                                     ignore_keys=['classes', 'palette']))
+
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco/',
+                 test_mode=True,
+                 ann_file='lvis/lvis_v1_minival_inserted_image_name.json',
+                 data_prefix=dict(img=''),
+                 batch_shapes_cfg=None),
+    class_text_path='data/texts/lvis_v1_class_texts.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='mmdet.LVISMetric',
+                     ann_file='data/coco/lvis/lvis_v1_minival_inserted_image_name.json',
+                     metric='bbox')
+test_evaluator = val_evaluator
+
+# training settings
+default_hooks = dict(param_scheduler=dict(max_epochs=max_epochs),
+                     checkpoint=dict(interval=save_epoch_intervals,
+                                     rule='greater'))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=10,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0)
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')

configs/pretrain/yolo_world_s_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py

@@ -0,0 +1,172 @@
+_base_ = ('../../third_party/mmyolo/configs/yolov8/'
+          'yolov8_s_syncbn_fast_8xb16-500e_coco.py')
+custom_imports = dict(imports=['yolo_world'],
+                      allow_failed_imports=False)
+
+# hyper-parameters
+num_classes = 1203
+num_training_classes = 80
+max_epochs = 100  # Maximum training epochs
+close_mosaic_epochs = 2
+save_epoch_intervals = 2
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-3
+weight_decay = 0.05 / 2
+train_batch_size_per_gpu = 16
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=['all'])),
+    neck=dict(type='YOLOWolrdDualPAFPN',
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'),
+              text_enhancder=dict(type='ImagePoolingAttentionModule',
+                                  embed_channels=256,
+                                  num_heads=8)),
+    bbox_head=dict(type='YOLOWorldHead',
+                   head_module=dict(type='YOLOWorldHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes)),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)))
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+train_pipeline = [
+    *_base_.pre_transform,
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=_base_.pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        max_aspect_ratio=_base_.max_aspect_ratio,
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114)),
+    *_base_.last_transform[:-1],
+    *text_transform,
+]
+train_pipeline_stage2 = [*_base_.train_pipeline_stage2[:-1], *text_transform]
+obj365v1_train_dataset = dict(
+    type='MultiModalDataset',
+    dataset=dict(
+        type='YOLOv5Objects365V1Dataset',
+        data_root='data/objects365v1/',
+        ann_file='annotations/objects365_train.json',
+        data_prefix=dict(img='train/'),
+        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
+    class_text_path='data/texts/obj365v1_class_texts.json',
+    pipeline=train_pipeline)
+
+mg_train_dataset = dict(type='YOLOv5MixedGroundingDataset',
+                        data_root='data/mixed_grounding/',
+                        ann_file='annotations/final_mixed_train_no_coco.json',
+                        data_prefix=dict(img='gqa/images/'),
+                        filter_cfg=dict(filter_empty_gt=False, min_size=32),
+                        pipeline=train_pipeline)
+
+flickr_train_dataset = dict(
+    type='YOLOv5MixedGroundingDataset',
+    data_root='data/flickr/',
+    ann_file='annotations/final_flickr_separateGT_train.json',
+    data_prefix=dict(img='full_images/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
+    pipeline=train_pipeline)
+
+train_dataloader = dict(batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=dict(_delete_=True,
+                                     type='ConcatDataset',
+                                     datasets=[
+                                         obj365v1_train_dataset,
+                                         flickr_train_dataset, mg_train_dataset
+                                     ],
+                                     ignore_keys=['classes', 'palette']))
+
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco/',
+                 test_mode=True,
+                 ann_file='lvis/lvis_v1_minival_inserted_image_name.json',
+                 data_prefix=dict(img=''),
+                 batch_shapes_cfg=None),
+    class_text_path='data/texts/lvis_v1_class_texts.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='mmdet.LVISMetric',
+                     ann_file='data/coco/lvis/lvis_v1_minival_inserted_image_name.json',
+                     metric='bbox')
+test_evaluator = val_evaluator
+
+# training settings
+default_hooks = dict(param_scheduler=dict(max_epochs=max_epochs),
+                     checkpoint=dict(interval=save_epoch_intervals,
+                                     rule='greater'))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=10,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0)
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')

configs/pretrain/yolo_world_x_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py

@@ -0,0 +1,172 @@
+_base_ = ('../../third_party/mmyolo/configs/yolov8/'
+          'yolov8_x_syncbn_fast_8xb16-500e_coco.py')
+custom_imports = dict(imports=['yolo_world'],
+                      allow_failed_imports=False)
+
+# hyper-parameters
+num_classes = 1203
+num_training_classes = 80
+max_epochs = 100  # Maximum training epochs
+close_mosaic_epochs = 2
+save_epoch_intervals = 2
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-3
+weight_decay = 0.05 / 2
+train_batch_size_per_gpu = 16
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=['all'])),
+    neck=dict(type='YOLOWolrdDualPAFPN',
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'),
+              text_enhancder=dict(type='ImagePoolingAttentionModule',
+                                  embed_channels=256,
+                                  num_heads=8)),
+    bbox_head=dict(type='YOLOWorldHead',
+                   head_module=dict(type='YOLOWorldHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes)),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)))
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+train_pipeline = [
+    *_base_.pre_transform,
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=_base_.pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        max_aspect_ratio=_base_.max_aspect_ratio,
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114)),
+    *_base_.last_transform[:-1],
+    *text_transform,
+]
+train_pipeline_stage2 = [*_base_.train_pipeline_stage2[:-1], *text_transform]
+obj365v1_train_dataset = dict(
+    type='MultiModalDataset',
+    dataset=dict(
+        type='YOLOv5Objects365V1Dataset',
+        data_root='data/objects365v1/',
+        ann_file='annotations/objects365_train.json',
+        data_prefix=dict(img='train/'),
+        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
+    class_text_path='data/texts/obj365v1_class_texts.json',
+    pipeline=train_pipeline)
+
+mg_train_dataset = dict(type='YOLOv5MixedGroundingDataset',
+                        data_root='data/mixed_grounding/',
+                        ann_file='annotations/final_mixed_train_no_coco.json',
+                        data_prefix=dict(img='gqa/images/'),
+                        filter_cfg=dict(filter_empty_gt=False, min_size=32),
+                        pipeline=train_pipeline)
+
+flickr_train_dataset = dict(
+    type='YOLOv5MixedGroundingDataset',
+    data_root='data/flickr/',
+    ann_file='annotations/final_flickr_separateGT_train.json',
+    data_prefix=dict(img='full_images/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
+    pipeline=train_pipeline)
+
+train_dataloader = dict(batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=dict(_delete_=True,
+                                     type='ConcatDataset',
+                                     datasets=[
+                                         obj365v1_train_dataset,
+                                         flickr_train_dataset, mg_train_dataset
+                                     ],
+                                     ignore_keys=['classes', 'palette']))
+
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco/',
+                 test_mode=True,
+                 ann_file='lvis/lvis_v1_minival_inserted_image_name.json',
+                 data_prefix=dict(img=''),
+                 batch_shapes_cfg=None),
+    class_text_path='data/texts/lvis_v1_class_texts.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='mmdet.LVISMetric',
+                     ann_file='data/coco/lvis/lvis_v1_minival_inserted_image_name.json',
+                     metric='bbox')
+test_evaluator = val_evaluator
+
+# training settings
+default_hooks = dict(param_scheduler=dict(max_epochs=max_epochs),
+                     checkpoint=dict(interval=save_epoch_intervals,
+                                     rule='greater'))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=10,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0)
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')

configs/segmentation/yolo_world_seg_l_dual_vlpan_2e-4_80e_8gpus_allmodules_finetune_lvis.py

@@ -0,0 +1,226 @@
+_base_ = (
+    '../../third_party/mmyolo/configs/yolov8/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py'
+)
+custom_imports = dict(imports=['yolo_world'], allow_failed_imports=False)
+# hyper-parameters
+num_classes = 1203
+num_training_classes = 80
+max_epochs = 80  # Maximum training epochs
+close_mosaic_epochs = 10
+save_epoch_intervals = 5
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-4
+
+weight_decay = 0.05
+train_batch_size_per_gpu = 8
+load_from = 'pretrained_models/yolo_world_l_clip_base_dual_vlpan_2e-3adamw_32xb16_100e_o365_goldg_train_pretrained-0e566235.pth'
+persistent_workers = False
+
+# Polygon2Mask
+downsample_ratio = 4
+mask_overlap = False
+use_mask2refine = True
+max_aspect_ratio = 100
+min_area_ratio = 0.01
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=[])),
+    neck=dict(type='YOLOWolrdDualPAFPN',
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'),
+              text_enhancder=dict(type='ImagePoolingAttentionModule',
+                                  embed_channels=256,
+                                  num_heads=8)),
+    bbox_head=dict(type='YOLOWorldSegHead',
+                   head_module=dict(type='YOLOWorldSegHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes,
+                                    mask_channels=32,
+                                    proto_channels=256),
+                   mask_overlap=mask_overlap,
+                   loss_mask=dict(type='mmdet.CrossEntropyLoss',
+                                  use_sigmoid=True,
+                                  reduction='none'),
+                   loss_mask_weight=1.0),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)),
+    test_cfg=dict(mask_thr_binary=0.5, fast_test=True))
+
+pre_transform = [
+    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
+    dict(type='LoadAnnotations',
+         with_bbox=True,
+         with_mask=True,
+         mask2bbox=True)
+]
+
+last_transform = [
+    dict(type='mmdet.Albu',
+         transforms=_base_.albu_train_transforms,
+         bbox_params=dict(type='BboxParams',
+                          format='pascal_voc',
+                          label_fields=['gt_bboxes_labels',
+                                        'gt_ignore_flags']),
+         keymap={
+             'img': 'image',
+             'gt_bboxes': 'bboxes'
+         }),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='Polygon2Mask',
+         downsample_ratio=downsample_ratio,
+         mask_overlap=mask_overlap),
+]
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+mosaic_affine_transform = [
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=pre_transform),
+    dict(type='YOLOv5CopyPaste', prob=_base_.copypaste_prob),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        max_aspect_ratio=100.,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        # img_scale is (width, height)
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        min_area_ratio=_base_.min_area_ratio,
+        use_mask_refine=True)
+]
+train_pipeline = [
+    *pre_transform, *mosaic_affine_transform,
+    dict(type='YOLOv5MultiModalMixUp',
+         prob=_base_.mixup_prob,
+         pre_transform=[*pre_transform, *mosaic_affine_transform]),
+    *last_transform, *text_transform
+]
+
+_train_pipeline_stage2 = [
+    *pre_transform,
+    dict(type='YOLOv5KeepRatioResize', scale=_base_.img_scale),
+    dict(type='LetterResize',
+         scale=_base_.img_scale,
+         allow_scale_up=True,
+         pad_val=dict(img=114.0)),
+    dict(type='YOLOv5RandomAffine',
+         max_rotate_degree=0.0,
+         max_shear_degree=0.0,
+         scaling_ratio_range=(1 - _base_.affine_scale,
+                              1 + _base_.affine_scale),
+         max_aspect_ratio=_base_.max_aspect_ratio,
+         border_val=(114, 114, 114),
+         min_area_ratio=min_area_ratio,
+         use_mask_refine=use_mask2refine), *last_transform
+]
+train_pipeline_stage2 = [*_train_pipeline_stage2, *text_transform]
+coco_train_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco',
+                 ann_file='lvis/lvis_v1_train_base.json',
+                 data_prefix=dict(img=''),
+                 filter_cfg=dict(filter_empty_gt=True, min_size=32)),
+    class_text_path='data/captions/lvis_v1_base_class_captions.json',
+    pipeline=train_pipeline)
+train_dataloader = dict(persistent_workers=persistent_workers,
+                        batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=coco_train_dataset)
+
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+
+# training settings
+default_hooks = dict(param_scheduler=dict(scheduler_type='linear',
+                                          lr_factor=0.01,
+                                          max_epochs=max_epochs),
+                     checkpoint=dict(max_keep_ckpts=-1,
+                                     save_best=None,
+                                     interval=save_epoch_intervals))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=5,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0),
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')
+
+# evaluation settings
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco/',
+                 test_mode=True,
+                 ann_file='lvis/lvis_v1_val.json',
+                 data_prefix=dict(img=''),
+                 batch_shapes_cfg=None),
+    class_text_path='data/captions/lvis_v1_class_captions.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='mmdet.LVISMetric',
+                     ann_file='data/coco/lvis/lvis_v1_val.json',
+                     metric=['bbox', 'segm'])
+test_evaluator = val_evaluator
+find_unused_parameters = True

configs/segmentation/yolo_world_seg_l_dual_vlpan_2e-4_80e_8gpus_seghead_finetune_lvis.py

@@ -0,0 +1,237 @@
+_base_ = (
+    '../../third_party/mmyolo/configs/yolov8/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py'
+)
+custom_imports = dict(imports=['yolo_world'], allow_failed_imports=False)
+# hyper-parameters
+num_classes = 1203
+num_training_classes = 80
+max_epochs = 80  # Maximum training epochs
+close_mosaic_epochs = 10
+save_epoch_intervals = 5
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-4
+
+weight_decay = 0.05
+train_batch_size_per_gpu = 8
+load_from = 'pretrained_models/yolo_world_l_clip_base_dual_vlpan_2e-3adamw_32xb16_100e_o365_goldg_train_pretrained-0e566235.pth'
+persistent_workers = False
+
+# Polygon2Mask
+downsample_ratio = 4
+mask_overlap = False
+use_mask2refine = True
+max_aspect_ratio = 100
+min_area_ratio = 0.01
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        frozen_stages=4,  # frozen the image backbone
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=['all'])),
+    neck=dict(type='YOLOWolrdDualPAFPN',
+              freeze_all=True,
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'),
+              text_enhancder=dict(type='ImagePoolingAttentionModule',
+                                  embed_channels=256,
+                                  num_heads=8)),
+    bbox_head=dict(type='YOLOWorldSegHead',
+                   head_module=dict(type='YOLOWorldSegHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes,
+                                    mask_channels=32,
+                                    proto_channels=256,
+                                    freeze_bbox=True),
+                   mask_overlap=mask_overlap,
+                   loss_mask=dict(type='mmdet.CrossEntropyLoss',
+                                  use_sigmoid=True,
+                                  reduction='none'),
+                   loss_mask_weight=1.0),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)),
+    test_cfg=dict(mask_thr_binary=0.5, fast_test=True))
+
+pre_transform = [
+    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
+    dict(type='LoadAnnotations',
+         with_bbox=True,
+         with_mask=True,
+         mask2bbox=True)
+]
+
+last_transform = [
+    dict(type='mmdet.Albu',
+         transforms=_base_.albu_train_transforms,
+         bbox_params=dict(type='BboxParams',
+                          format='pascal_voc',
+                          label_fields=['gt_bboxes_labels',
+                                        'gt_ignore_flags']),
+         keymap={
+             'img': 'image',
+             'gt_bboxes': 'bboxes'
+         }),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='Polygon2Mask',
+         downsample_ratio=downsample_ratio,
+         mask_overlap=mask_overlap),
+]
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+mosaic_affine_transform = [
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=pre_transform),
+    dict(type='YOLOv5CopyPaste', prob=_base_.copypaste_prob),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        max_aspect_ratio=100.,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        # img_scale is (width, height)
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        min_area_ratio=_base_.min_area_ratio,
+        use_mask_refine=True)
+]
+train_pipeline = [
+    *pre_transform, *mosaic_affine_transform,
+    dict(type='YOLOv5MultiModalMixUp',
+         prob=_base_.mixup_prob,
+         pre_transform=[*pre_transform, *mosaic_affine_transform]),
+    *last_transform, *text_transform
+]
+
+_train_pipeline_stage2 = [
+    *pre_transform,
+    dict(type='YOLOv5KeepRatioResize', scale=_base_.img_scale),
+    dict(type='LetterResize',
+         scale=_base_.img_scale,
+         allow_scale_up=True,
+         pad_val=dict(img=114.0)),
+    dict(type='YOLOv5RandomAffine',
+         max_rotate_degree=0.0,
+         max_shear_degree=0.0,
+         scaling_ratio_range=(1 - _base_.affine_scale,
+                              1 + _base_.affine_scale),
+         max_aspect_ratio=_base_.max_aspect_ratio,
+         border_val=(114, 114, 114),
+         min_area_ratio=min_area_ratio,
+         use_mask_refine=use_mask2refine), *last_transform
+]
+train_pipeline_stage2 = [*_train_pipeline_stage2, *text_transform]
+coco_train_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco',
+                 ann_file='lvis/lvis_v1_train_base.json',
+                 data_prefix=dict(img=''),
+                 filter_cfg=dict(filter_empty_gt=True, min_size=32)),
+    class_text_path='data/captions/lvis_v1_base_class_captions.json',
+    pipeline=train_pipeline)
+train_dataloader = dict(persistent_workers=persistent_workers,
+                        batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=coco_train_dataset)
+
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+
+# training settings
+default_hooks = dict(param_scheduler=dict(scheduler_type='linear',
+                                          lr_factor=0.01,
+                                          max_epochs=max_epochs),
+                     checkpoint=dict(max_keep_ckpts=-1,
+                                     save_best=None,
+                                     interval=save_epoch_intervals))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=5,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0),
+                                            'neck':
+                                            dict(lr_mult=0.0),
+                                            'head.head_module.reg_preds':
+                                            dict(lr_mult=0.0),
+                                            'head.head_module.cls_preds':
+                                            dict(lr_mult=0.0),
+                                            'head.head_module.cls_contrasts':
+                                            dict(lr_mult=0.0)
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')
+
+# evaluation settings
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco/',
+                 test_mode=True,
+                 ann_file='lvis/lvis_v1_val.json',
+                 data_prefix=dict(img=''),
+                 batch_shapes_cfg=None),
+    class_text_path='data/captions/lvis_v1_class_captions.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='mmdet.LVISMetric',
+                     ann_file='data/coco/lvis/lvis_v1_val.json',
+                     metric=['bbox', 'segm'])
+test_evaluator = val_evaluator
+find_unused_parameters = True

configs/segmentation/yolo_world_seg_m_dual_vlpan_2e-4_80e_8gpus_allmodules_finetune_lvis.py

@@ -0,0 +1,226 @@
+_base_ = (
+    '../../third_party/mmyolo/configs/yolov8/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco.py'
+)
+custom_imports = dict(imports=['yolo_world'], allow_failed_imports=False)
+# hyper-parameters
+num_classes = 1203
+num_training_classes = 80
+max_epochs = 80  # Maximum training epochs
+close_mosaic_epochs = 10
+save_epoch_intervals = 5
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-4
+
+weight_decay = 0.05
+train_batch_size_per_gpu = 8
+load_from = 'pretrained_models/yolo_world_m_clip_base_dual_vlpan_2e-3adamw_32xb16_100e_o365_goldg_train_pretrained-2b7bd1be.pth'
+persistent_workers = False
+
+# Polygon2Mask
+downsample_ratio = 4
+mask_overlap = False
+use_mask2refine = True
+max_aspect_ratio = 100
+min_area_ratio = 0.01
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=[])),
+    neck=dict(type='YOLOWolrdDualPAFPN',
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'),
+              text_enhancder=dict(type='ImagePoolingAttentionModule',
+                                  embed_channels=256,
+                                  num_heads=8)),
+    bbox_head=dict(type='YOLOWorldSegHead',
+                   head_module=dict(type='YOLOWorldSegHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes,
+                                    mask_channels=32,
+                                    proto_channels=256),
+                   mask_overlap=mask_overlap,
+                   loss_mask=dict(type='mmdet.CrossEntropyLoss',
+                                  use_sigmoid=True,
+                                  reduction='none'),
+                   loss_mask_weight=1.0),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)),
+    test_cfg=dict(mask_thr_binary=0.5, fast_test=True))
+
+pre_transform = [
+    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
+    dict(type='LoadAnnotations',
+         with_bbox=True,
+         with_mask=True,
+         mask2bbox=True)
+]
+
+last_transform = [
+    dict(type='mmdet.Albu',
+         transforms=_base_.albu_train_transforms,
+         bbox_params=dict(type='BboxParams',
+                          format='pascal_voc',
+                          label_fields=['gt_bboxes_labels',
+                                        'gt_ignore_flags']),
+         keymap={
+             'img': 'image',
+             'gt_bboxes': 'bboxes'
+         }),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='Polygon2Mask',
+         downsample_ratio=downsample_ratio,
+         mask_overlap=mask_overlap),
+]
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+mosaic_affine_transform = [
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=pre_transform),
+    dict(type='YOLOv5CopyPaste', prob=_base_.copypaste_prob),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        max_aspect_ratio=100.,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        # img_scale is (width, height)
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        min_area_ratio=_base_.min_area_ratio,
+        use_mask_refine=True)
+]
+train_pipeline = [
+    *pre_transform, *mosaic_affine_transform,
+    dict(type='YOLOv5MultiModalMixUp',
+         prob=_base_.mixup_prob,
+         pre_transform=[*pre_transform, *mosaic_affine_transform]),
+    *last_transform, *text_transform
+]
+
+_train_pipeline_stage2 = [
+    *pre_transform,
+    dict(type='YOLOv5KeepRatioResize', scale=_base_.img_scale),
+    dict(type='LetterResize',
+         scale=_base_.img_scale,
+         allow_scale_up=True,
+         pad_val=dict(img=114.0)),
+    dict(type='YOLOv5RandomAffine',
+         max_rotate_degree=0.0,
+         max_shear_degree=0.0,
+         scaling_ratio_range=(1 - _base_.affine_scale,
+                              1 + _base_.affine_scale),
+         max_aspect_ratio=_base_.max_aspect_ratio,
+         border_val=(114, 114, 114),
+         min_area_ratio=min_area_ratio,
+         use_mask_refine=use_mask2refine), *last_transform
+]
+train_pipeline_stage2 = [*_train_pipeline_stage2, *text_transform]
+coco_train_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco',
+                 ann_file='lvis/lvis_v1_train_base.json',
+                 data_prefix=dict(img=''),
+                 filter_cfg=dict(filter_empty_gt=True, min_size=32)),
+    class_text_path='data/captions/lvis_v1_base_class_captions.json',
+    pipeline=train_pipeline)
+train_dataloader = dict(persistent_workers=persistent_workers,
+                        batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=coco_train_dataset)
+
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+
+# training settings
+default_hooks = dict(param_scheduler=dict(scheduler_type='linear',
+                                          lr_factor=0.01,
+                                          max_epochs=max_epochs),
+                     checkpoint=dict(max_keep_ckpts=-1,
+                                     save_best=None,
+                                     interval=save_epoch_intervals))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=5,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0)
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')
+
+# evaluation settings
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco/',
+                 test_mode=True,
+                 ann_file='lvis/lvis_v1_val.json',
+                 data_prefix=dict(img=''),
+                 batch_shapes_cfg=None),
+    class_text_path='data/captions/lvis_v1_class_captions.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='mmdet.LVISMetric',
+                     ann_file='data/coco/lvis/lvis_v1_val.json',
+                     metric=['bbox', 'segm'])
+test_evaluator = val_evaluator
+find_unused_parameters = True

configs/segmentation/yolo_world_seg_m_dual_vlpan_2e-4_80e_8gpus_seghead_finetune_lvis.py

@@ -0,0 +1,237 @@
+_base_ = (
+    '../../third_party/mmyolo/configs/yolov8/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco.py'
+)
+custom_imports = dict(imports=['yolo_world'], allow_failed_imports=False)
+# hyper-parameters
+num_classes = 1203
+num_training_classes = 80
+max_epochs = 80  # Maximum training epochs
+close_mosaic_epochs = 10
+save_epoch_intervals = 5
+text_channels = 512
+neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
+neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
+base_lr = 2e-4
+
+weight_decay = 0.05
+train_batch_size_per_gpu = 8
+load_from = 'pretrained_models/yolo_world_m_clip_base_dual_vlpan_2e-3adamw_32xb16_100e_o365_goldg_train_pretrained-2b7bd1be.pth'
+persistent_workers = False
+
+# Polygon2Mask
+downsample_ratio = 4
+mask_overlap = False
+use_mask2refine = True
+max_aspect_ratio = 100
+min_area_ratio = 0.01
+
+# model settings
+model = dict(
+    type='YOLOWorldDetector',
+    mm_neck=True,
+    num_train_classes=num_training_classes,
+    num_test_classes=num_classes,
+    data_preprocessor=dict(type='YOLOWDetDataPreprocessor'),
+    backbone=dict(
+        _delete_=True,
+        type='MultiModalYOLOBackbone',
+        image_model={{_base_.model.backbone}},
+        frozen_stages=4,  # frozen the image backbone
+        text_model=dict(
+            type='HuggingCLIPLanguageBackbone',
+            model_name='openai/clip-vit-base-patch32',
+            frozen_modules=['all'])),
+    neck=dict(type='YOLOWolrdDualPAFPN',
+              freeze_all=True,
+              guide_channels=text_channels,
+              embed_channels=neck_embed_channels,
+              num_heads=neck_num_heads,
+              block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'),
+              text_enhancder=dict(type='ImagePoolingAttentionModule',
+                                  embed_channels=256,
+                                  num_heads=8)),
+    bbox_head=dict(type='YOLOWorldSegHead',
+                   head_module=dict(type='YOLOWorldSegHeadModule',
+                                    embed_dims=text_channels,
+                                    num_classes=num_training_classes,
+                                    mask_channels=32,
+                                    proto_channels=256,
+                                    freeze_bbox=True),
+                   mask_overlap=mask_overlap,
+                   loss_mask=dict(type='mmdet.CrossEntropyLoss',
+                                  use_sigmoid=True,
+                                  reduction='none'),
+                   loss_mask_weight=1.0),
+    train_cfg=dict(assigner=dict(num_classes=num_training_classes)),
+    test_cfg=dict(mask_thr_binary=0.5, fast_test=True))
+
+pre_transform = [
+    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
+    dict(type='LoadAnnotations',
+         with_bbox=True,
+         with_mask=True,
+         mask2bbox=True)
+]
+
+last_transform = [
+    dict(type='mmdet.Albu',
+         transforms=_base_.albu_train_transforms,
+         bbox_params=dict(type='BboxParams',
+                          format='pascal_voc',
+                          label_fields=['gt_bboxes_labels',
+                                        'gt_ignore_flags']),
+         keymap={
+             'img': 'image',
+             'gt_bboxes': 'bboxes'
+         }),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='Polygon2Mask',
+         downsample_ratio=downsample_ratio,
+         mask_overlap=mask_overlap),
+]
+
+# dataset settings
+text_transform = [
+    dict(type='RandomLoadText',
+         num_neg_samples=(num_classes, num_classes),
+         max_num_samples=num_training_classes,
+         padding_to_max=True,
+         padding_value=''),
+    dict(type='PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                    'flip_direction', 'texts'))
+]
+mosaic_affine_transform = [
+    dict(type='MultiModalMosaic',
+         img_scale=_base_.img_scale,
+         pad_val=114.0,
+         pre_transform=pre_transform),
+    dict(type='YOLOv5CopyPaste', prob=_base_.copypaste_prob),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        max_aspect_ratio=100.,
+        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
+        # img_scale is (width, height)
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        min_area_ratio=_base_.min_area_ratio,
+        use_mask_refine=True)
+]
+train_pipeline = [
+    *pre_transform, *mosaic_affine_transform,
+    dict(type='YOLOv5MultiModalMixUp',
+         prob=_base_.mixup_prob,
+         pre_transform=[*pre_transform, *mosaic_affine_transform]),
+    *last_transform, *text_transform
+]
+
+_train_pipeline_stage2 = [
+    *pre_transform,
+    dict(type='YOLOv5KeepRatioResize', scale=_base_.img_scale),
+    dict(type='LetterResize',
+         scale=_base_.img_scale,
+         allow_scale_up=True,
+         pad_val=dict(img=114.0)),
+    dict(type='YOLOv5RandomAffine',
+         max_rotate_degree=0.0,
+         max_shear_degree=0.0,
+         scaling_ratio_range=(1 - _base_.affine_scale,
+                              1 + _base_.affine_scale),
+         max_aspect_ratio=_base_.max_aspect_ratio,
+         border_val=(114, 114, 114),
+         min_area_ratio=min_area_ratio,
+         use_mask_refine=use_mask2refine), *last_transform
+]
+train_pipeline_stage2 = [*_train_pipeline_stage2, *text_transform]
+coco_train_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco',
+                 ann_file='lvis/lvis_v1_train_base.json',
+                 data_prefix=dict(img=''),
+                 filter_cfg=dict(filter_empty_gt=True, min_size=32)),
+    class_text_path='data/captions/lvis_v1_base_class_captions.json',
+    pipeline=train_pipeline)
+train_dataloader = dict(persistent_workers=persistent_workers,
+                        batch_size=train_batch_size_per_gpu,
+                        collate_fn=dict(type='yolow_collate'),
+                        dataset=coco_train_dataset)
+
+test_pipeline = [
+    *_base_.test_pipeline[:-1],
+    dict(type='LoadText'),
+    dict(type='mmdet.PackDetInputs',
+         meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                    'scale_factor', 'pad_param', 'texts'))
+]
+
+# training settings
+default_hooks = dict(param_scheduler=dict(scheduler_type='linear',
+                                          lr_factor=0.01,
+                                          max_epochs=max_epochs),
+                     checkpoint=dict(max_keep_ckpts=-1,
+                                     save_best=None,
+                                     interval=save_epoch_intervals))
+custom_hooks = [
+    dict(type='EMAHook',
+         ema_type='ExpMomentumEMA',
+         momentum=0.0001,
+         update_buffers=True,
+         strict_load=False,
+         priority=49),
+    dict(type='mmdet.PipelineSwitchHook',
+         switch_epoch=max_epochs - close_mosaic_epochs,
+         switch_pipeline=train_pipeline_stage2)
+]
+train_cfg = dict(max_epochs=max_epochs,
+                 val_interval=5,
+                 dynamic_intervals=[((max_epochs - close_mosaic_epochs),
+                                     _base_.val_interval_stage2)])
+optim_wrapper = dict(optimizer=dict(
+    _delete_=True,
+    type='AdamW',
+    lr=base_lr,
+    weight_decay=weight_decay,
+    batch_size_per_gpu=train_batch_size_per_gpu),
+                     paramwise_cfg=dict(bias_decay_mult=0.0,
+                                        norm_decay_mult=0.0,
+                                        custom_keys={
+                                            'backbone.text_model':
+                                            dict(lr_mult=0.01),
+                                            'logit_scale':
+                                            dict(weight_decay=0.0),
+                                            'neck':
+                                            dict(lr_mult=0.0),
+                                            'head.head_module.reg_preds':
+                                            dict(lr_mult=0.0),
+                                            'head.head_module.cls_preds':
+                                            dict(lr_mult=0.0),
+                                            'head.head_module.cls_contrasts':
+                                            dict(lr_mult=0.0)
+                                        }),
+                     constructor='YOLOWv5OptimizerConstructor')
+
+# evaluation settings
+coco_val_dataset = dict(
+    _delete_=True,
+    type='MultiModalDataset',
+    dataset=dict(type='YOLOv5LVISV1Dataset',
+                 data_root='data/coco/',
+                 test_mode=True,
+                 ann_file='lvis/lvis_v1_val.json',
+                 data_prefix=dict(img=''),
+                 batch_shapes_cfg=None),
+    class_text_path='data/captions/lvis_v1_class_captions.json',
+    pipeline=test_pipeline)
+val_dataloader = dict(dataset=coco_val_dataset)
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='mmdet.LVISMetric',
+                     ann_file='data/coco/lvis/lvis_v1_val.json',
+                     metric=['bbox', 'segm'])
+test_evaluator = val_evaluator
+find_unused_parameters = True

data/texts/coco_class_texts.json

@@ -0,0 +1 @@
+[["person"], ["bicycle"], ["car"], ["motorcycle"], ["airplane"], ["bus"], ["train"], ["truck"], ["boat"], ["traffic light"], ["fire hydrant"], ["stop sign"], ["parking meter"], ["bench"], ["bird"], ["cat"], ["dog"], ["horse"], ["sheep"], ["cow"], ["elephant"], ["bear"], ["zebra"], ["giraffe"], ["backpack"], ["umbrella"], ["handbag"], ["tie"], ["suitcase"], ["frisbee"], ["skis"], ["snowboard"], ["sports ball"], ["kite"], ["baseball bat"], ["baseball glove"], ["skateboard"], ["surfboard"], ["tennis racket"], ["bottle"], ["wine glass"], ["cup"], ["fork"], ["knife"], ["spoon"], ["bowl"], ["banana"], ["apple"], ["sandwich"], ["orange"], ["broccoli"], ["carrot"], ["hot dog"], ["pizza"], ["donut"], ["cake"], ["chair"], ["couch"], ["potted plant"], ["bed"], ["dining table"], ["toilet"], ["tv"], ["laptop"], ["mouse"], ["remote"], ["keyboard"], ["cell phone"], ["microwave"], ["oven"], ["toaster"], ["sink"], ["refrigerator"], ["book"], ["clock"], ["vase"], ["scissors"], ["teddy bear"], ["hair drier"], ["toothbrush"]]

data/texts/lvis_v1_class_texts.json

@@ -0,0 +1 @@
+[["aerosol can", "spray can"], ["air conditioner"], ["airplane", "aeroplane"], ["alarm clock"], ["alcohol", "alcoholic beverage"], ["alligator", "gator"], ["almond"], ["ambulance"], ["amplifier"], ["anklet", "ankle bracelet"], ["antenna", "aerial", "transmitting aerial"], ["apple"], ["applesauce"], ["apricot"], ["apron"], ["aquarium", "fish tank"], ["arctic", "arctic type of shoe", "galosh", "golosh", "rubber", "rubber type of shoe", "gumshoe"], ["armband"], ["armchair"], ["armoire"], ["armor", "armour"], ["artichoke"], ["trash can", "garbage can", "wastebin", "dustbin", "trash barrel", "trash bin"], ["ashtray"], ["asparagus"], ["atomizer", "atomiser", "spray", "sprayer", "nebulizer", "nebuliser"], ["avocado"], ["award", "accolade"], ["awning"], ["ax", "axe"], ["baboon"], ["baby buggy", "baby carriage", "perambulator", "pram", "stroller"], ["basketball backboard"], ["backpack", "knapsack", "packsack", "rucksack", "haversack"], ["handbag", "purse", "pocketbook"], ["suitcase", "baggage", "luggage"], ["bagel", "beigel"], ["bagpipe"], ["baguet", "baguette"], ["bait", "lure"], ["ball"], ["ballet skirt", "tutu"], ["balloon"], ["bamboo"], ["banana"], ["Band Aid"], ["bandage"], ["bandanna", "bandana"], ["banjo"], ["banner", "streamer"], ["barbell"], ["barge"], ["barrel", "cask"], ["barrette"], ["barrow", "garden cart", "lawn cart", "wheelbarrow"], ["baseball base"], ["baseball"], ["baseball bat"], ["baseball cap", "jockey cap", "golf cap"], ["baseball glove", "baseball mitt"], ["basket", "handbasket"], ["basketball"], ["bass horn", "sousaphone", "tuba"], ["bat", "bat animal"], ["bath mat"], ["bath towel"], ["bathrobe"], ["bathtub", "bathing tub"], ["batter", "batter food"], ["battery"], ["beachball"], ["bead"], ["bean curd", "tofu"], ["beanbag"], ["beanie", "beany"], ["bear"], ["bed"], ["bedpan"], ["bedspread", "bedcover", "bed covering", "counterpane", "spread"], ["cow"], ["beef", "beef food", "boeuf", "boeuf food"], ["beeper", "pager"], ["beer bottle"], ["beer can"], ["beetle"], ["bell"], ["bell pepper", "capsicum"], ["belt"], ["belt buckle"], ["bench"], ["beret"], ["bib"], ["Bible"], ["bicycle", "bike", "bike bicycle"], ["visor", "vizor"], ["billboard"], ["binder", "ring-binder"], ["binoculars", "field glasses", "opera glasses"], ["bird"], ["birdfeeder"], ["birdbath"], ["birdcage"], ["birdhouse"], ["birthday cake"], ["birthday card"], ["pirate flag"], ["black sheep"], ["blackberry"], ["blackboard", "chalkboard"], ["blanket"], ["blazer", "sport jacket", "sport coat", "sports jacket", "sports coat"], ["blender", "liquidizer", "liquidiser"], ["blimp"], ["blinker", "flasher"], ["blouse"], ["blueberry"], ["gameboard"], ["boat", "ship", "ship boat"], ["bob", "bobber", "bobfloat"], ["bobbin", "spool", "reel"], ["bobby pin", "hairgrip"], ["boiled egg", "coddled egg"], ["bolo tie", "bolo", "bola tie", "bola"], ["deadbolt"], ["bolt"], ["bonnet"], ["book"], ["bookcase"], ["booklet", "brochure", "leaflet", "pamphlet"], ["bookmark", "bookmarker"], ["boom microphone", "microphone boom"], ["boot"], ["bottle"], ["bottle opener"], ["bouquet"], ["bow", "bow weapon"], ["bow", "bow decorative ribbons"], ["bow-tie", "bowtie"], ["bowl"], ["pipe bowl"], ["bowler hat", "bowler", "derby hat", "derby", "plug hat"], ["bowling ball"], ["box"], ["boxing glove"], ["suspenders"], ["bracelet", "bangle"], ["brass plaque"], ["brassiere", "bra", "bandeau"], ["bread-bin", "breadbox"], ["bread"], ["breechcloth", "breechclout", "loincloth"], ["bridal gown", "wedding gown", "wedding dress"], ["briefcase"], ["broccoli"], ["broach"], ["broom"], ["brownie"], ["brussels sprouts"], ["bubble gum"], ["bucket", "pail"], ["horse buggy"], ["horned cow"], ["bulldog"], ["bulldozer", "dozer"], ["bullet train"], ["bulletin board", "notice board"], ["bulletproof vest"], ["bullhorn", "megaphone"], ["bun", "roll"], ["bunk bed"], ["buoy"], ["burrito"], ["bus", "bus vehicle", "autobus", "charabanc", "double-decker", "motorbus", "motorcoach"], ["business card"], ["butter"], ["butterfly"], ["button"], ["cab", "cab taxi", "taxi", "taxicab"], ["cabana"], ["cabin car", "caboose"], ["cabinet"], ["locker", "storage locker"], ["cake"], ["calculator"], ["calendar"], ["calf"], ["camcorder"], ["camel"], ["camera"], ["camera lens"], ["camper", "camper vehicle", "camping bus", "motor home"], ["can", "tin can"], ["can opener", "tin opener"], ["candle", "candlestick"], ["candle holder"], ["candy bar"], ["candy cane"], ["walking cane"], ["canister", "cannister"], ["canoe"], ["cantaloup", "cantaloupe"], ["canteen"], ["cap", "cap headwear"], ["bottle cap", "cap", "cap container lid"], ["cape"], ["cappuccino", "coffee cappuccino"], ["car", "car automobile", "auto", "auto automobile", "automobile"], ["railcar", "railcar part of a train", "railway car", "railway car part of a train", "railroad car", "railroad car part of a train"], ["elevator car"], ["car battery", "automobile battery"], ["identity card"], ["card"], ["cardigan"], ["cargo ship", "cargo vessel"], ["carnation"], ["horse carriage"], ["carrot"], ["tote bag"], ["cart"], ["carton"], ["cash register", "register", "register for cash transactions"], ["casserole"], ["cassette"], ["cast", "plaster cast", "plaster bandage"], ["cat"], ["cauliflower"], ["cayenne", "cayenne spice", "cayenne pepper", "cayenne pepper spice", "red pepper", "red pepper spice"], ["CD player"], ["celery"], ["cellular telephone", "cellular phone", "cellphone", "mobile phone", "smart phone"], ["chain mail", "ring mail", "chain armor", "chain armour", "ring armor", "ring armour"], ["chair"], ["chaise longue", "chaise", "daybed"], ["chalice"], ["chandelier"], ["chap"], ["checkbook", "chequebook"], ["checkerboard"], ["cherry"], ["chessboard"], ["chicken", "chicken animal"], ["chickpea", "garbanzo"], ["chili", "chili vegetable", "chili pepper", "chili pepper vegetable", "chilli", "chilli vegetable", "chilly", "chilly vegetable", "chile", "chile vegetable"], ["chime", "gong"], ["chinaware"], ["crisp", "crisp potato chip", "potato chip"], ["poker chip"], ["chocolate bar"], ["chocolate cake"], ["chocolate milk"], ["chocolate mousse"], ["choker", "collar", "neckband"], ["chopping board", "cutting board", "chopping block"], ["chopstick"], ["Christmas tree"], ["slide"], ["cider", "cyder"], ["cigar box"], ["cigarette"], ["cigarette case", "cigarette pack"], ["cistern", "water tank"], ["clarinet"], ["clasp"], ["cleansing agent", "cleanser", "cleaner"], ["cleat", "cleat for securing rope"], ["clementine"], ["clip"], ["clipboard"], ["clippers", "clippers for plants"], ["cloak"], ["clock", "timepiece", "timekeeper"], ["clock tower"], ["clothes hamper", "laundry basket", "clothes basket"], ["clothespin", "clothes peg"], ["clutch bag"], ["coaster"], ["coat"], ["coat hanger", "clothes hanger", "dress hanger"], ["coatrack", "hatrack"], ["cock", "rooster"], ["cockroach"], ["cocoa", "cocoa beverage", "hot chocolate", "hot chocolate beverage", "drinking chocolate"], ["coconut", "cocoanut"], ["coffee maker", "coffee machine"], ["coffee table", "cocktail table"], ["coffeepot"], ["coil"], ["coin"], ["colander", "cullender"], ["coleslaw", "slaw"], ["coloring material", "colouring material"], ["combination lock"], ["pacifier", "teething ring"], ["comic book"], ["compass"], ["computer keyboard", "keyboard", "keyboard computer"], ["condiment"], ["cone", "traffic cone"], ["control", "controller"], ["convertible", "convertible automobile"], ["sofa bed"], ["cooker"], ["cookie", "cooky", "biscuit", "biscuit cookie"], ["cooking utensil"], ["cooler", "cooler for food", "ice chest"], ["cork", "cork bottle plug", "bottle cork"], ["corkboard"], ["corkscrew", "bottle screw"], ["edible corn", "corn", "maize"], ["cornbread"], ["cornet", "horn", "trumpet"], ["cornice", "valance", "valance board", "pelmet"], ["cornmeal"], ["corset", "girdle"], ["costume"], ["cougar", "puma", "catamount", "mountain lion", "panther"], ["coverall"], ["cowbell"], ["cowboy hat", "ten-gallon hat"], ["crab", "crab animal"], ["crabmeat"], ["cracker"], ["crape", "crepe", "French pancake"], ["crate"], ["crayon", "wax crayon"], ["cream pitcher"], ["crescent roll", "croissant"], ["crib", "cot"], ["crock pot", "earthenware jar"], ["crossbar"], ["crouton"], ["crow"], ["crowbar", "wrecking bar", "pry bar"], ["crown"], ["crucifix"], ["cruise ship", "cruise liner"], ["police cruiser", "patrol car", "police car", "squad car"], ["crumb"], ["crutch"], ["cub", "cub animal"], ["cube", "square block"], ["cucumber", "cuke"], ["cufflink"], ["cup"], ["trophy cup"], ["cupboard", "closet"], ["cupcake"], ["hair curler", "hair roller", "hair crimper"], ["curling iron"], ["curtain", "drapery"], ["cushion"], ["cylinder"], ["cymbal"], ["dagger"], ["dalmatian"], ["dartboard"], ["date", "date fruit"], ["deck chair", "beach chair"], ["deer", "cervid"], ["dental floss", "floss"], ["desk"], ["detergent"], ["diaper"], ["diary", "journal"], ["die", "dice"], ["dinghy", "dory", "rowboat"], ["dining table"], ["tux", "tuxedo"], ["dish"], ["dish antenna"], ["dishrag", "dishcloth"], ["dishtowel", "tea towel"], ["dishwasher", "dishwashing machine"], ["dishwasher detergent", "dishwashing detergent", "dishwashing liquid", "dishsoap"], ["dispenser"], ["diving board"], ["Dixie cup", "paper cup"], ["dog"], ["dog collar"], ["doll"], ["dollar", "dollar bill", "one dollar bill"], ["dollhouse", "doll's house"], ["dolphin"], ["domestic ass", "donkey"], ["doorknob", "doorhandle"], ["doormat", "welcome mat"], ["doughnut", "donut"], ["dove"], ["dragonfly"], ["drawer"], ["underdrawers", "boxers", "boxershorts"], ["dress", "frock"], ["dress hat", "high hat", "opera hat", "silk hat", "top hat"], ["dress suit"], ["dresser"], ["drill"], ["drone"], ["dropper", "eye dropper"], ["drum", "drum musical instrument"], ["drumstick"], ["duck"], ["duckling"], ["duct tape"], ["duffel bag", "duffle bag", "duffel", "duffle"], ["dumbbell"], ["dumpster"], ["dustpan"], ["eagle"], ["earphone", "earpiece", "headphone"], ["earplug"], ["earring"], ["easel"], ["eclair"], ["eel"], ["egg", "eggs"], ["egg roll", "spring roll"], ["egg yolk", "yolk", "yolk egg"], ["eggbeater", "eggwhisk"], ["eggplant", "aubergine"], ["electric chair"], ["refrigerator"], ["elephant"], ["elk", "moose"], ["envelope"], ["eraser"], ["escargot"], ["eyepatch"], ["falcon"], ["fan"], ["faucet", "spigot", "tap"], ["fedora"], ["ferret"], ["Ferris wheel"], ["ferry", "ferryboat"], ["fig", "fig fruit"], ["fighter jet", "fighter aircraft", "attack aircraft"], ["figurine"], ["file cabinet", "filing cabinet"], ["file", "file tool"], ["fire alarm", "smoke alarm"], ["fire engine", "fire truck"], ["fire extinguisher", "extinguisher"], ["fire hose"], ["fireplace"], ["fireplug", "fire hydrant", "hydrant"], ["first-aid kit"], ["fish"], ["fish", "fish food"], ["fishbowl", "goldfish bowl"], ["fishing rod", "fishing pole"], ["flag"], ["flagpole", "flagstaff"], ["flamingo"], ["flannel"], ["flap"], ["flash", "flashbulb"], ["flashlight", "torch"], ["fleece"], ["flip-flop", "flip-flop sandal"], ["flipper", "flipper footwear", "fin", "fin footwear"], ["flower arrangement", "floral arrangement"], ["flute glass", "champagne flute"], ["foal"], ["folding chair"], ["food processor"], ["football", "football American"], ["football helmet"], ["footstool", "footrest"], ["fork"], ["forklift"], ["freight car"], ["French toast"], ["freshener", "air freshener"], ["frisbee"], ["frog", "toad", "toad frog"], ["fruit juice"], ["frying pan", "frypan", "skillet"], ["fudge"], ["funnel"], ["futon"], ["gag", "muzzle"], ["garbage"], ["garbage truck"], ["garden hose"], ["gargle", "mouthwash"], ["gargoyle"], ["garlic", "ail"], ["gasmask", "respirator", "gas helmet"], ["gazelle"], ["gelatin", "jelly"], ["gemstone"], ["generator"], ["giant panda", "panda", "panda bear"], ["gift wrap"], ["ginger", "gingerroot"], ["giraffe"], ["cincture", "sash", "waistband", "waistcloth"], ["glass", "glass drink container", "drinking glass"], ["globe"], ["glove"], ["goat"], ["goggles"], ["goldfish"], ["golf club", "golf-club"], ["golfcart"], ["gondola", "gondola boat"], ["goose"], ["gorilla"], ["gourd"], ["grape"], ["grater"], ["gravestone", "headstone", "tombstone"], ["gravy boat", "gravy holder"], ["green bean"], ["green onion", "spring onion", "scallion"], ["griddle"], ["grill", "grille", "grillwork", "radiator grille"], ["grits", "hominy grits"], ["grizzly", "grizzly bear"], ["grocery bag"], ["guitar"], ["gull", "seagull"], ["gun"], ["hairbrush"], ["hairnet"], ["hairpin"], ["halter top"], ["ham", "jambon", "gammon"], ["hamburger", "beefburger", "burger"], ["hammer"], ["hammock"], ["hamper"], ["hamster"], ["hair dryer"], ["hand glass", "hand mirror"], ["hand towel", "face towel"], ["handcart", "pushcart", "hand truck"], ["handcuff"], ["handkerchief"], ["handle", "grip", "handgrip"], ["handsaw", "carpenter's saw"], ["hardback book", "hardcover book"], ["harmonium", "organ", "organ musical instrument", "reed organ", "reed organ musical instrument"], ["hat"], ["hatbox"], ["veil"], ["headband"], ["headboard"], ["headlight", "headlamp"], ["headscarf"], ["headset"], ["headstall", "headstall for horses", "headpiece", "headpiece for horses"], ["heart"], ["heater", "warmer"], ["helicopter"], ["helmet"], ["heron"], ["highchair", "feeding chair"], ["hinge"], ["hippopotamus"], ["hockey stick"], ["hog", "pig"], ["home plate", "home plate baseball", "home base", "home base baseball"], ["honey"], ["fume hood", "exhaust hood"], ["hook"], ["hookah", "narghile", "nargileh", "sheesha", "shisha", "water pipe"], ["hornet"], ["horse"], ["hose", "hosepipe"], ["hot-air balloon"], ["hotplate"], ["hot sauce"], ["hourglass"], ["houseboat"], ["hummingbird"], ["hummus", "humus", "hommos", "hoummos", "humous"], ["polar bear"], ["icecream"], ["popsicle"], ["ice maker"], ["ice pack", "ice bag"], ["ice skate"], ["igniter", "ignitor", "lighter"], ["inhaler", "inhalator"], ["iPod"], ["iron", "iron for clothing", "smoothing iron", "smoothing iron for clothing"], ["ironing board"], ["jacket"], ["jam"], ["jar"], ["jean", "blue jean", "denim"], ["jeep", "landrover"], ["jelly bean", "jelly egg"], ["jersey", "T-shirt", "tee shirt"], ["jet plane", "jet-propelled plane"], ["jewel", "gem", "precious stone"], ["jewelry", "jewellery"], ["joystick"], ["jumpsuit"], ["kayak"], ["keg"], ["kennel", "doghouse"], ["kettle", "boiler"], ["key"], ["keycard"], ["kilt"], ["kimono"], ["kitchen sink"], ["kitchen table"], ["kite"], ["kitten", "kitty"], ["kiwi fruit"], ["knee pad"], ["knife"], ["knitting needle"], ["knob"], ["knocker", "knocker on a door", "doorknocker"], ["koala", "koala bear"], ["lab coat", "laboratory coat"], ["ladder"], ["ladle"], ["ladybug", "ladybeetle", "ladybird beetle"], ["lamb", "lamb animal"], ["lamb-chop", "lambchop"], ["lamp"], ["lamppost"], ["lampshade"], ["lantern"], ["lanyard", "laniard"], ["laptop computer", "notebook computer"], ["lasagna", "lasagne"], ["latch"], ["lawn mower"], ["leather"], ["legging", "legging clothing", "leging", "leging clothing", "leg covering"], ["Lego", "Lego set"], ["legume"], ["lemon"], ["lemonade"], ["lettuce"], ["license plate", "numberplate"], ["life buoy", "lifesaver", "life belt", "life ring"], ["life jacket", "life vest"], ["lightbulb"], ["lightning rod", "lightning conductor"], ["lime"], ["limousine"], ["lion"], ["lip balm"], ["liquor", "spirits", "hard liquor", "liqueur", "cordial"], ["lizard"], ["log"], ["lollipop"], ["speaker", "speaker stereo equipment"], ["loveseat"], ["machine gun"], ["magazine"], ["magnet"], ["mail slot"], ["mailbox", "mailbox at home", "letter box", "letter box at home"], ["mallard"], ["mallet"], ["mammoth"], ["manatee"], ["mandarin orange"], ["manger", "trough"], ["manhole"], ["map"], ["marker"], ["martini"], ["mascot"], ["mashed potato"], ["masher"], ["mask", "facemask"], ["mast"], ["mat", "mat gym equipment", "gym mat"], ["matchbox"], ["mattress"], ["measuring cup"], ["measuring stick", "ruler", "ruler measuring stick", "measuring rod"], ["meatball"], ["medicine"], ["melon"], ["microphone"], ["microscope"], ["microwave oven"], ["milestone", "milepost"], ["milk"], ["milk can"], ["milkshake"], ["minivan"], ["mint candy"], ["mirror"], ["mitten"], ["mixer", "mixer kitchen tool", "stand mixer"], ["money"], ["monitor", "monitor computer equipment"], ["monkey"], ["motor"], ["motor scooter", "scooter"], ["motor vehicle", "automotive vehicle"], ["motorcycle"], ["mound", "mound baseball", "pitcher's mound"], ["mouse", "mouse computer equipment", "computer mouse"], ["mousepad"], ["muffin"], ["mug"], ["mushroom"], ["music stool", "piano stool"], ["musical instrument", "instrument", "instrument musical"], ["nailfile"], ["napkin", "table napkin", "serviette"], ["neckerchief"], ["necklace"], ["necktie", "tie", "tie necktie"], ["needle"], ["nest"], ["newspaper", "paper", "paper newspaper"], ["newsstand"], ["nightshirt", "nightwear", "sleepwear", "nightclothes"], ["nosebag", "nosebag for animals", "feedbag"], ["noseband", "noseband for animals", "nosepiece", "nosepiece for animals"], ["notebook"], ["notepad"], ["nut"], ["nutcracker"], ["oar"], ["octopus", "octopus food"], ["octopus", "octopus animal"], ["oil lamp", "kerosene lamp", "kerosine lamp"], ["olive oil"], ["omelet", "omelette"], ["onion"], ["orange", "orange fruit"], ["orange juice"], ["ostrich"], ["ottoman", "pouf", "pouffe", "hassock"], ["oven"], ["overalls", "overalls clothing"], ["owl"], ["packet"], ["inkpad", "inking pad", "stamp pad"], ["pad"], ["paddle", "boat paddle"], ["padlock"], ["paintbrush"], ["painting"], ["pajamas", "pyjamas"], ["palette", "pallet"], ["pan", "pan for cooking", "cooking pan"], ["pan", "pan metal container"], ["pancake"], ["pantyhose"], ["papaya"], ["paper plate"], ["paper towel"], ["paperback book", "paper-back book", "softback book", "soft-cover book"], ["paperweight"], ["parachute"], ["parakeet", "parrakeet", "parroket", "paraquet", "paroquet", "parroquet"], ["parasail", "parasail sports"], ["parasol", "sunshade"], ["parchment"], ["parka", "anorak"], ["parking meter"], ["parrot"], ["passenger car", "passenger car part of a train", "coach", "coach part of a train"], ["passenger ship"], ["passport"], ["pastry"], ["patty", "patty food"], ["pea", "pea food"], ["peach"], ["peanut butter"], ["pear"], ["peeler", "peeler tool for fruit and vegetables"], ["wooden leg", "pegleg"], ["pegboard"], ["pelican"], ["pen"], ["pencil"], ["pencil box", "pencil case"], ["pencil sharpener"], ["pendulum"], ["penguin"], ["pennant"], ["penny", "penny coin"], ["pepper", "peppercorn"], ["pepper mill", "pepper grinder"], ["perfume"], ["persimmon"], ["person", "baby", "child", "boy", "girl", "man", "woman", "human"], ["pet"], ["pew", "pew church bench", "church bench"], ["phonebook", "telephone book", "telephone directory"], ["phonograph record", "phonograph recording", "record", "record phonograph recording"], ["piano"], ["pickle"], ["pickup truck"], ["pie"], ["pigeon"], ["piggy bank", "penny bank"], ["pillow"], ["pin", "pin non jewelry"], ["pineapple"], ["pinecone"], ["ping-pong ball"], ["pinwheel"], ["tobacco pipe"], ["pipe", "piping"], ["pistol", "handgun"], ["pita", "pita bread", "pocket bread"], ["pitcher", "pitcher vessel for liquid", "ewer"], ["pitchfork"], ["pizza"], ["place mat"], ["plate"], ["platter"], ["playpen"], ["pliers", "plyers"], ["plow", "plow farm equipment", "plough", "plough farm equipment"], ["plume"], ["pocket watch"], ["pocketknife"], ["poker", "poker fire stirring tool", "stove poker", "fire hook"], ["pole", "post"], ["polo shirt", "sport shirt"], ["poncho"], ["pony"], ["pool table", "billiard table", "snooker table"], ["pop", "pop soda", "soda", "soda pop", "tonic", "soft drink"], ["postbox", "postbox public", "mailbox", "mailbox public"], ["postcard", "postal card", "mailing-card"], ["poster", "placard"], ["pot"], ["flowerpot"], ["potato"], ["potholder"], ["pottery", "clayware"], ["pouch"], ["power shovel", "excavator", "digger"], ["prawn", "shrimp"], ["pretzel"], ["printer", "printing machine"], ["projectile", "projectile weapon", "missile"], ["projector"], ["propeller", "propellor"], ["prune"], ["pudding"], ["puffer", "puffer fish", "pufferfish", "blowfish", "globefish"], ["puffin"], ["pug-dog"], ["pumpkin"], ["puncher"], ["puppet", "marionette"], ["puppy"], ["quesadilla"], ["quiche"], ["quilt", "comforter"], ["rabbit"], ["race car", "racing car"], ["racket", "racquet"], ["radar"], ["radiator"], ["radio receiver", "radio set", "radio", "tuner", "tuner radio"], ["radish", "daikon"], ["raft"], ["rag doll"], ["raincoat", "waterproof jacket"], ["ram", "ram animal"], ["raspberry"], ["rat"], ["razorblade"], ["reamer", "reamer juicer", "juicer", "juice reamer"], ["rearview mirror"], ["receipt"], ["recliner", "reclining chair", "lounger", "lounger chair"], ["record player", "phonograph", "phonograph record player", "turntable"], ["reflector"], ["remote control"], ["rhinoceros"], ["rib", "rib food"], ["rifle"], ["ring"], ["river boat"], ["road map"], ["robe"], ["rocking chair"], ["rodent"], ["roller skate"], ["Rollerblade"], ["rolling pin"], ["root beer"], ["router", "router computer equipment"], ["rubber band", "elastic band"], ["runner", "runner carpet"], ["plastic bag", "paper bag"], ["saddle", "saddle on an animal"], ["saddle blanket", "saddlecloth", "horse blanket"], ["saddlebag"], ["safety pin"], ["sail"], ["salad"], ["salad plate", "salad bowl"], ["salami"], ["salmon", "salmon fish"], ["salmon", "salmon food"], ["salsa"], ["saltshaker"], ["sandal", "sandal type of shoe"], ["sandwich"], ["satchel"], ["saucepan"], ["saucer"], ["sausage"], ["sawhorse", "sawbuck"], ["saxophone"], ["scale", "scale measuring instrument"], ["scarecrow", "strawman"], ["scarf"], ["school bus"], ["scissors"], ["scoreboard"], ["scraper"], ["screwdriver"], ["scrubbing brush"], ["sculpture"], ["seabird", "seafowl"], ["seahorse"], ["seaplane", "hydroplane"], ["seashell"], ["sewing machine"], ["shaker"], ["shampoo"], ["shark"], ["sharpener"], ["Sharpie"], ["shaver", "shaver electric", "electric shaver", "electric razor"], ["shaving cream", "shaving soap"], ["shawl"], ["shears"], ["sheep"], ["shepherd dog", "sheepdog"], ["sherbert", "sherbet"], ["shield"], ["shirt"], ["shoe", "sneaker", "sneaker type of shoe", "tennis shoe"], ["shopping bag"], ["shopping cart"], ["short pants", "shorts", "shorts clothing", "trunks", "trunks clothing"], ["shot glass"], ["shoulder bag"], ["shovel"], ["shower head"], ["shower cap"], ["shower curtain"], ["shredder", "shredder for paper"], ["signboard"], ["silo"], ["sink"], ["skateboard"], ["skewer"], ["ski"], ["ski boot"], ["ski parka", "ski jacket"], ["ski pole"], ["skirt"], ["skullcap"], ["sled", "sledge", "sleigh"], ["sleeping bag"], ["sling", "sling bandage", "triangular bandage"], ["slipper", "slipper footwear", "carpet slipper", "carpet slipper footwear"], ["smoothie"], ["snake", "serpent"], ["snowboard"], ["snowman"], ["snowmobile"], ["soap"], ["soccer ball"], ["sock"], ["sofa", "couch", "lounge"], ["softball"], ["solar array", "solar battery", "solar panel"], ["sombrero"], ["soup"], ["soup bowl"], ["soupspoon"], ["sour cream", "soured cream"], ["soya milk", "soybean milk", "soymilk"], ["space shuttle"], ["sparkler", "sparkler fireworks"], ["spatula"], ["spear", "lance"], ["spectacles", "specs", "eyeglasses", "glasses"], ["spice rack"], ["spider"], ["crawfish", "crayfish"], ["sponge"], ["spoon"], ["sportswear", "athletic wear", "activewear"], ["spotlight"], ["squid", "squid food", "calamari", "calamary"], ["squirrel"], ["stagecoach"], ["stapler", "stapler stapling machine"], ["starfish", "sea star"], ["statue", "statue sculpture"], ["steak", "steak food"], ["steak knife"], ["steering wheel"], ["stepladder"], ["step stool"], ["stereo", "stereo sound system"], ["stew"], ["stirrer"], ["stirrup"], ["stool"], ["stop sign"], ["brake light"], ["stove", "kitchen stove", "range", "range kitchen appliance", "kitchen range", "cooking stove"], ["strainer"], ["strap"], ["straw", "straw for drinking", "drinking straw"], ["strawberry"], ["street sign"], ["streetlight", "street lamp"], ["string cheese"], ["stylus"], ["subwoofer"], ["sugar bowl"], ["sugarcane", "sugarcane plant"], ["suit", "suit clothing"], ["sunflower"], ["sunglasses"], ["sunhat"], ["surfboard"], ["sushi"], ["mop"], ["sweat pants"], ["sweatband"], ["sweater"], ["sweatshirt"], ["sweet potato"], ["swimsuit", "swimwear", "bathing suit", "swimming costume", "bathing costume", "swimming trunks", "bathing trunks"], ["sword"], ["syringe"], ["Tabasco sauce"], ["table-tennis table", "ping-pong table"], ["table"], ["table lamp"], ["tablecloth"], ["tachometer"], ["taco"], ["tag"], ["taillight", "rear light"], ["tambourine"], ["army tank", "armored combat vehicle", "armoured combat vehicle"], ["tank", "tank storage vessel", "storage tank"], ["tank top", "tank top clothing"], ["tape", "tape sticky cloth or paper"], ["tape measure", "measuring tape"], ["tapestry"], ["tarp"], ["tartan", "plaid"], ["tassel"], ["tea bag"], ["teacup"], ["teakettle"], ["teapot"], ["teddy bear"], ["telephone", "phone", "telephone set"], ["telephone booth", "phone booth", "call box", "telephone box", "telephone kiosk"], ["telephone pole", "telegraph pole", "telegraph post"], ["telephoto lens", "zoom lens"], ["television camera", "tv camera"], ["television set", "tv", "tv set"], ["tennis ball"], ["tennis racket"], ["tequila"], ["thermometer"], ["thermos bottle"], ["thermostat"], ["thimble"], ["thread", "yarn"], ["thumbtack", "drawing pin", "pushpin"], ["tiara"], ["tiger"], ["tights", "tights clothing", "leotards"], ["timer", "stopwatch"], ["tinfoil"], ["tinsel"], ["tissue paper"], ["toast", "toast food"], ["toaster"], ["toaster oven"], ["toilet"], ["toilet tissue", "toilet paper", "bathroom tissue"], ["tomato"], ["tongs"], ["toolbox"], ["toothbrush"], ["toothpaste"], ["toothpick"], ["cover"], ["tortilla"], ["tow truck"], ["towel"], ["towel rack", "towel rail", "towel bar"], ["toy"], ["tractor", "tractor farm equipment"], ["traffic light"], ["dirt bike"], ["trailer truck", "tractor trailer", "trucking rig", "articulated lorry", "semi truck"], ["train", "train railroad vehicle", "railroad train"], ["trampoline"], ["tray"], ["trench coat"], ["triangle", "triangle musical instrument"], ["tricycle"], ["tripod"], ["trousers", "pants", "pants clothing"], ["truck"], ["truffle", "truffle chocolate", "chocolate truffle"], ["trunk"], ["vat"], ["turban"], ["turkey", "turkey food"], ["turnip"], ["turtle"], ["turtleneck", "turtleneck clothing", "polo-neck"], ["typewriter"], ["umbrella"], ["underwear", "underclothes", "underclothing", "underpants"], ["unicycle"], ["urinal"], ["urn"], ["vacuum cleaner"], ["vase"], ["vending machine"], ["vent", "blowhole", "air vent"], ["vest", "waistcoat"], ["videotape"], ["vinegar"], ["violin", "fiddle"], ["vodka"], ["volleyball"], ["vulture"], ["waffle"], ["waffle iron"], ["wagon"], ["wagon wheel"], ["walking stick"], ["wall clock"], ["wall socket", "wall plug", "electric outlet", "electrical outlet", "outlet", "electric receptacle"], ["wallet", "billfold"], ["walrus"], ["wardrobe"], ["washbasin", "basin", "basin for washing", "washbowl", "washstand", "handbasin"], ["automatic washer", "washing machine"], ["watch", "wristwatch"], ["water bottle"], ["water cooler"], ["water faucet", "water tap", "tap", "tap water faucet"], ["water heater", "hot-water heater"], ["water jug"], ["water gun", "squirt gun"], ["water scooter", "sea scooter", "jet ski"], ["water ski"], ["water tower"], ["watering can"], ["watermelon"], ["weathervane", "vane", "vane weathervane", "wind vane"], ["webcam"], ["wedding cake", "bridecake"], ["wedding ring", "wedding band"], ["wet suit"], ["wheel"], ["wheelchair"], ["whipped cream"], ["whistle"], ["wig"], ["wind chime"], ["windmill"], ["window box", "window box for plants"], ["windshield wiper", "windscreen wiper", "wiper", "wiper for windshield or screen"], ["windsock", "air sock", "air-sleeve", "wind sleeve", "wind cone"], ["wine bottle"], ["wine bucket", "wine cooler"], ["wineglass"], ["blinder", "blinder for horses"], ["wok"], ["wolf"], ["wooden spoon"], ["wreath"], ["wrench", "spanner"], ["wristband"], ["wristlet", "wrist band"], ["yacht"], ["yogurt", "yoghurt", "yoghourt"], ["yoke", "yoke animal equipment"], ["zebra"], ["zucchini", "courgette"]]

data/texts/obj365v1_class_texts.json

@@ -0,0 +1 @@
+[["person"], ["sneakers"], ["chair"], ["hat"], ["lamp"], ["bottle"], ["cabinet", "shelf"], ["cup"], ["car"], ["glasses"], ["picture", "frame"], ["desk"], ["handbag"], ["street lights"], ["book"], ["plate"], ["helmet"], ["leather shoes"], ["pillow"], ["glove"], ["potted plant"], ["bracelet"], ["flower"], ["tv"], ["storage box"], ["vase"], ["bench"], ["wine glass"], ["boots"], ["bowl"], ["dining table"], ["umbrella"], ["boat"], ["flag"], ["speaker"], ["trash bin", "can"], ["stool"], ["backpack"], ["couch"], ["belt"], ["carpet"], ["basket"], ["towel", "napkin"], ["slippers"], ["barrel", "bucket"], ["coffee table"], ["suv"], ["toy"], ["tie"], ["bed"], ["traffic light"], ["pen", "pencil"], ["microphone"], ["sandals"], ["canned"], ["necklace"], ["mirror"], ["faucet"], ["bicycle"], ["bread"], ["high heels"], ["ring"], ["van"], ["watch"], ["sink"], ["horse"], ["fish"], ["apple"], ["camera"], ["candle"], ["teddy bear"], ["cake"], ["motorcycle"], ["wild bird"], ["laptop"], ["knife"], ["traffic sign"], ["cell phone"], ["paddle"], ["truck"], ["cow"], ["power outlet"], ["clock"], ["drum"], ["fork"], ["bus"], ["hanger"], ["nightstand"], ["pot", "pan"], ["sheep"], ["guitar"], ["traffic cone"], ["tea pot"], ["keyboard"], ["tripod"], ["hockey"], ["fan"], ["dog"], ["spoon"], ["blackboard", "whiteboard"], ["balloon"], ["air conditioner"], ["cymbal"], ["mouse"], ["telephone"], ["pickup truck"], ["orange"], ["banana"], ["airplane"], ["luggage"], ["skis"], ["soccer"], ["trolley"], ["oven"], ["remote"], ["baseball glove"], ["paper towel"], ["refrigerator"], ["train"], ["tomato"], ["machinery vehicle"], ["tent"], ["shampoo", "shower gel"], ["head phone"], ["lantern"], ["donut"], ["cleaning products"], ["sailboat"], ["tangerine"], ["pizza"], ["kite"], ["computer box"], ["elephant"], ["toiletries"], ["gas stove"], ["broccoli"], ["toilet"], ["stroller"], ["shovel"], ["baseball bat"], ["microwave"], ["skateboard"], ["surfboard"], ["surveillance camera"], ["gun"], ["life saver"], ["cat"], ["lemon"], ["liquid soap"], ["zebra"], ["duck"], ["sports car"], ["giraffe"], ["pumpkin"], ["piano"], ["stop sign"], ["radiator"], ["converter"], ["tissue"], ["carrot"], ["washing machine"], ["vent"], ["cookies"], ["cutting", "chopping board"], ["tennis racket"], ["candy"], ["skating and skiing shoes"], ["scissors"], ["folder"], ["baseball"], ["strawberry"], ["bow tie"], ["pigeon"], ["pepper"], ["coffee machine"], ["bathtub"], ["snowboard"], ["suitcase"], ["grapes"], ["ladder"], ["pear"], ["american football"], ["basketball"], ["potato"], ["paint brush"], ["printer"], ["billiards"], ["fire hydrant"], ["goose"], ["projector"], ["sausage"], ["fire extinguisher"], ["extension cord"], ["facial mask"], ["tennis ball"], ["chopsticks"], ["electronic stove and gas stove"], ["pie"], ["frisbee"], ["kettle"], ["hamburger"], ["golf club"], ["cucumber"], ["clutch"], ["blender"], ["tong"], ["slide"], ["hot dog"], ["toothbrush"], ["facial cleanser"], ["mango"], ["deer"], ["egg"], ["violin"], ["marker"], ["ship"], ["chicken"], ["onion"], ["ice cream"], ["tape"], ["wheelchair"], ["plum"], ["bar soap"], ["scale"], ["watermelon"], ["cabbage"], ["router", "modem"], ["golf ball"], ["pine apple"], ["crane"], ["fire truck"], ["peach"], ["cello"], ["notepaper"], ["tricycle"], ["toaster"], ["helicopter"], ["green beans"], ["brush"], ["carriage"], ["cigar"], ["earphone"], ["penguin"], ["hurdle"], ["swing"], ["radio"], ["cd"], ["parking meter"], ["swan"], ["garlic"], ["french fries"], ["horn"], ["avocado"], ["saxophone"], ["trumpet"], ["sandwich"], ["cue"], ["kiwi fruit"], ["bear"], ["fishing rod"], ["cherry"], ["tablet"], ["green vegetables"], ["nuts"], ["corn"], ["key"], ["screwdriver"], ["globe"], ["broom"], ["pliers"], ["volleyball"], ["hammer"], ["eggplant"], ["trophy"], ["dates"], ["board eraser"], ["rice"], ["tape measure", "ruler"], ["dumbbell"], ["hamimelon"], ["stapler"], ["camel"], ["lettuce"], ["goldfish"], ["meat balls"], ["medal"], ["toothpaste"], ["antelope"], ["shrimp"], ["rickshaw"], ["trombone"], ["pomegranate"], ["coconut"], ["jellyfish"], ["mushroom"], ["calculator"], ["treadmill"], ["butterfly"], ["egg tart"], ["cheese"], ["pig"], ["pomelo"], ["race car"], ["rice cooker"], ["tuba"], ["crosswalk sign"], ["papaya"], ["hair drier"], ["green onion"], ["chips"], ["dolphin"], ["sushi"], ["urinal"], ["donkey"], ["electric drill"], ["spring rolls"], ["tortoise", "turtle"], ["parrot"], ["flute"], ["measuring cup"], ["shark"], ["steak"], ["poker card"], ["binoculars"], ["llama"], ["radish"], ["noodles"], ["yak"], ["mop"], ["crab"], ["microscope"], ["barbell"], ["bread", "bun"], ["baozi"], ["lion"], ["red cabbage"], ["polar bear"], ["lighter"], ["seal"], ["mangosteen"], ["comb"], ["eraser"], ["pitaya"], ["scallop"], ["pencil case"], ["saw"], ["table tennis paddle"], ["okra"], ["starfish"], ["eagle"], ["monkey"], ["durian"], ["game board"], ["rabbit"], ["french horn"], ["ambulance"], ["asparagus"], ["hoverboard"], ["pasta"], ["target"], ["hotair balloon"], ["chainsaw"], ["lobster"], ["iron"], ["flashlight"]]

pyproject.toml

@@ -0,0 +1,56 @@
+[build-system]
+requires = ["setuptools","wheel","torch"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "yolo_world"
+version = "0.1.0"  # Replace with your actual version
+description = "YOLO-World: Real-time Open Vocabulary Object Detection"
+readme = "README.md"
+keywords = ["object detection"]
+authors = [
+    { name = "Tencent AILab", email = "ronnysong@tencent.com" },
+]
+license = {text = "Apache License 2.0"}
+
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
+requires-python = ">= 3.7"
+
+dependencies = [
+    "wheel",
+    "torch",
+    "torchvision",
+    "transformers",
+    "tokenizers",
+    "numpy",
+    "opencv-python",
+    "supervision==0.18.0",
+    "openmim",
+    "mmcv-lite>=2.0.0rc4,<2.1.0",
+    "mmdet>=3.0.0",
+    "mmengine>=0.7.1",
+    "mmcv",
+    'mmyolo @ git+https://github.com/onuralpszr/mmyolo.git',
+
+]
+
+[tool.setuptools]
+package-dir = {"yolo_world" = "yolo_world"}
+include-package-data = false
+license-files = ["LICENSE"]
+zip-safe = true
+
+[tool.setuptools.packages.find]
+include = ["yolo_world*"]
+exclude = ["docs*", "tests*","third_party*","assets*"]

requirements.txt

@@ -1,20 +1,14 @@
 openmim
-mmcv-lite
-mmdet>=3.0.0
-mmengine>=0.7.1
 gradio
 transformers
 numpy
 opencv-python
 supervision
-ftfy
-regex
-pot
-sentencepiece
-tokenizers
+wheel
 
 --extra-index-url https://download.pytorch.org/whl/cu121
-torch
-torchvision
---extra-index-url https://download.openmmlab.com/mmcv/dist/cu121/torch2.1/index.html
-mmcv
+torch==2.1.0+cu121
+torchdata==0.7.0
+torchsummary==1.5.1
+torchtext==0.16.0
+torchvision==0.16.0+cu121

third_party/mmyolo/.circleci/config.yml

@@ -0,0 +1,34 @@
+version: 2.1
+
+# this allows you to use CircleCI's dynamic configuration feature
+setup: true
+
+# the path-filtering orb is required to continue a pipeline based on
+# the path of an updated fileset
+orbs:
+  path-filtering: circleci/path-filtering@0.1.2
+
+workflows:
+  # the always-run workflow is always triggered, regardless of the pipeline parameters.
+  always-run:
+    jobs:
+      # the path-filtering/filter job determines which pipeline
+      # parameters to update.
+      - path-filtering/filter:
+          name: check-updated-files
+          # 3-column, whitespace-delimited mapping. One mapping per
+          # line:
+          # <regex path-to-test> <parameter-to-set> <value-of-pipeline-parameter>
+          mapping: |
+            mmyolo/.* lint_only false
+            requirements/.* lint_only false
+            tests/.* lint_only false
+            tools/.* lint_only false
+            configs/.* lint_only false
+            .circleci/.* lint_only false
+          base-revision: main
+          # this is the path of the configuration we should trigger once
+          # path filtering and pipeline parameter value updates are
+          # complete. In this case, we are using the parent dynamic
+          # configuration itself.
+          config-path: .circleci/test.yml

third_party/mmyolo/.circleci/docker/Dockerfile

@@ -0,0 +1,11 @@
+ARG PYTORCH="1.8.1"
+ARG CUDA="10.2"
+ARG CUDNN="7"
+
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+# To fix GPG key error when running apt-get update
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+
+RUN apt-get update && apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx

third_party/mmyolo/.circleci/test.yml

@@ -0,0 +1,213 @@
+version: 2.1
+
+# the default pipeline parameters, which will be updated according to
+# the results of the path-filtering orb
+parameters:
+  lint_only:
+    type: boolean
+    default: true
+
+jobs:
+  lint:
+    docker:
+      - image: cimg/python:3.7.4
+    steps:
+      - checkout
+      - run:
+          name: Install pre-commit hook
+          command: |
+            pip install pre-commit
+            pre-commit install
+      - run:
+          name: Linting
+          command: pre-commit run --all-files
+      - run:
+          name: Check docstring coverage
+          command: |
+            pip install interrogate
+            interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 90 mmyolo
+  build_cpu:
+    parameters:
+      # The python version must match available image tags in
+      # https://circleci.com/developer/images/image/cimg/python
+      python:
+        type: string
+      torch:
+        type: string
+      torchvision:
+        type: string
+    docker:
+      - image: cimg/python:<< parameters.python >>
+    resource_class: large
+    steps:
+      - checkout
+      - run:
+          name: Install Libraries
+          command: |
+            sudo apt-get update
+            sudo apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx libjpeg-dev zlib1g-dev libtinfo-dev libncurses5
+      - run:
+          name: Configure Python & pip
+          command: |
+            pip install --upgrade pip
+            pip install wheel
+      - run:
+          name: Install PyTorch
+          command: |
+            python -V
+            pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - run:
+          name: Install ONNXRuntime
+          command: |
+            pip install onnxruntime==1.8.1
+            wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-1.8.1.tgz
+            tar xvf onnxruntime-linux-x64-1.8.1.tgz
+      - run:
+          name: Install mmyolo dependencies
+          command: |
+            pip install -U openmim
+            mim install git+https://github.com/open-mmlab/mmengine.git@main
+            mim install 'mmcv >= 2.0.0'
+            mim install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+            pip install -r requirements/albu.txt
+            pip install -r requirements/tests.txt
+      - run:
+          name: Install mmdeploy
+          command: |
+            pip install setuptools
+            git clone -b dev-1.x --depth 1 https://github.com/open-mmlab/mmdeploy.git mmdeploy --recurse-submodules
+            wget https://github.com/Kitware/CMake/releases/download/v3.20.0/cmake-3.20.0-linux-x86_64.tar.gz
+            tar -xzvf cmake-3.20.0-linux-x86_64.tar.gz
+            sudo ln -sf $(pwd)/cmake-3.20.0-linux-x86_64/bin/* /usr/bin/
+            cd mmdeploy && mkdir build && cd build && cmake .. -DMMDEPLOY_TARGET_BACKENDS=ort -DONNXRUNTIME_DIR=/home/circleci/project/onnxruntime-linux-x64-1.8.1 && make -j8 && make install
+            export LD_LIBRARY_PATH=/home/circleci/project/onnxruntime-linux-x64-1.8.1/lib:${LD_LIBRARY_PATH}
+            cd /home/circleci/project/mmdeploy && python -m pip install -v -e .
+      - run:
+          name: Build and install
+          command: |
+            pip install -e .
+      - run:
+          name: Run unittests
+          command: |
+            export LD_LIBRARY_PATH=/home/circleci/project/onnxruntime-linux-x64-1.8.1/lib:${LD_LIBRARY_PATH}
+            pytest tests/
+#            coverage run --branch --source mmyolo -m pytest tests/
+#            coverage xml
+#            coverage report -m
+  build_cuda:
+    parameters:
+      torch:
+        type: string
+      cuda:
+        type: enum
+        enum: ["10.1", "10.2", "11.0", "11.7"]
+      cudnn:
+        type: integer
+        default: 7
+    machine:
+      image: ubuntu-2004-cuda-11.4:202110-01
+      # docker_layer_caching: true
+    resource_class: gpu.nvidia.small
+    steps:
+      - checkout
+      - run:
+          # Cloning repos in VM since Docker doesn't have access to the private key
+          name: Clone Repos
+          command: |
+            git clone -b main --depth 1 https://github.com/open-mmlab/mmengine.git /home/circleci/mmengine
+            git clone -b dev-3.x --depth 1 https://github.com/open-mmlab/mmdetection.git /home/circleci/mmdetection
+      - run:
+          name: Build Docker image
+          command: |
+            docker build .circleci/docker -t mmyolo:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
+            docker run --gpus all -t -d -v /home/circleci/project:/mmyolo -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmdetection:/mmdetection -w /mmyolo --name mmyolo mmyolo:gpu
+      - run:
+          name: Install mmyolo dependencies
+          command: |
+            docker exec mmyolo pip install -U openmim
+            docker exec mmyolo mim install -e /mmengine
+            docker exec mmyolo mim install 'mmcv >= 2.0.0'
+            docker exec mmyolo pip install -e /mmdetection
+            docker exec mmyolo pip install -r requirements/albu.txt
+            docker exec mmyolo pip install -r requirements/tests.txt
+      - run:
+          name: Build and install
+          command: |
+            docker exec mmyolo pip install -e .
+      - run:
+          name: Run unittests
+          command: |
+            docker exec mmyolo pytest tests/
+
+workflows:
+  pr_stage_lint:
+    when: << pipeline.parameters.lint_only >>
+    jobs:
+      - lint:
+          name: lint
+          filters:
+            branches:
+              ignore:
+                - main
+
+  pr_stage_test:
+    when:
+      not: << pipeline.parameters.lint_only >>
+    jobs:
+      - lint:
+          name: lint
+          filters:
+            branches:
+              ignore:
+                - main
+      - build_cpu:
+          name: minimum_version_cpu
+          torch: 1.8.0
+          torchvision: 0.9.0
+          python: 3.8.0 # The lowest python 3.7.x version available on CircleCI images
+          requires:
+            - lint
+      - build_cpu:
+          name: maximum_version_cpu
+          # mmdeploy not supported
+#          torch: 2.0.0
+#          torchvision: 0.15.1
+          torch: 1.12.1
+          torchvision: 0.13.1
+          python: 3.9.0
+          requires:
+            - minimum_version_cpu
+      - hold:
+          type: approval
+          requires:
+            - maximum_version_cpu
+      - build_cuda:
+          name: mainstream_version_gpu
+          torch: 1.8.1
+          # Use double quotation mark to explicitly specify its type
+          # as string instead of number
+          cuda: "10.2"
+          requires:
+            - hold
+      - build_cuda:
+          name: maximum_version_gpu
+          torch: 2.0.0
+          cuda: "11.7"
+          cudnn: 8
+          requires:
+            - hold
+  merge_stage_test:
+    when:
+      not: << pipeline.parameters.lint_only >>
+    jobs:
+      - build_cuda:
+          name: minimum_version_gpu
+          torch: 1.7.0
+          # Use double quotation mark to explicitly specify its type
+          # as string instead of number
+          cuda: "11.0"
+          cudnn: 8
+          filters:
+            branches:
+              only:
+                - main

third_party/mmyolo/.dev_scripts/gather_models.py

@@ -0,0 +1,312 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import os
+import os.path as osp
+import shutil
+import subprocess
+import time
+from collections import OrderedDict
+
+import torch
+import yaml
+from mmengine.config import Config
+from mmengine.fileio import dump
+from mmengine.utils import mkdir_or_exist, scandir
+
+
+def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds):
+
+    class OrderedDumper(Dumper):
+        pass
+
+    def _dict_representer(dumper, data):
+        return dumper.represent_mapping(
+            yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, data.items())
+
+    OrderedDumper.add_representer(OrderedDict, _dict_representer)
+    return yaml.dump(data, stream, OrderedDumper, **kwds)
+
+
+def process_checkpoint(in_file, out_file):
+    checkpoint = torch.load(in_file, map_location='cpu')
+    # remove optimizer for smaller file size
+    if 'optimizer' in checkpoint:
+        del checkpoint['optimizer']
+    if 'message_hub' in checkpoint:
+        del checkpoint['message_hub']
+    if 'ema_state_dict' in checkpoint:
+        del checkpoint['ema_state_dict']
+
+    for key in list(checkpoint['state_dict']):
+        if key.startswith('data_preprocessor'):
+            checkpoint['state_dict'].pop(key)
+        elif 'priors_base_sizes' in key:
+            checkpoint['state_dict'].pop(key)
+        elif 'grid_offset' in key:
+            checkpoint['state_dict'].pop(key)
+        elif 'prior_inds' in key:
+            checkpoint['state_dict'].pop(key)
+
+    # if it is necessary to remove some sensitive data in checkpoint['meta'],
+    # add the code here.
+    if torch.__version__ >= '1.6':
+        torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False)
+    else:
+        torch.save(checkpoint, out_file)
+    sha = subprocess.check_output(['sha256sum', out_file]).decode()
+    final_file = out_file.rstrip('.pth') + f'-{sha[:8]}.pth'
+    subprocess.Popen(['mv', out_file, final_file])
+    return final_file
+
+
+def is_by_epoch(config):
+    cfg = Config.fromfile('./configs/' + config)
+    return cfg.train_cfg.type == 'EpochBasedTrainLoop'
+
+
+def get_final_epoch_or_iter(config):
+    cfg = Config.fromfile('./configs/' + config)
+    if cfg.train_cfg.type == 'EpochBasedTrainLoop':
+        return cfg.train_cfg.max_epochs
+    else:
+        return cfg.train_cfg.max_iters
+
+
+def get_best_epoch_or_iter(exp_dir):
+    best_epoch_iter_full_path = list(
+        sorted(glob.glob(osp.join(exp_dir, 'best_*.pth'))))[-1]
+    best_epoch_or_iter_model_path = best_epoch_iter_full_path.split('/')[-1]
+    best_epoch_or_iter = best_epoch_or_iter_model_path. \
+        split('_')[-1].split('.')[0]
+    return best_epoch_or_iter_model_path, int(best_epoch_or_iter)
+
+
+def get_real_epoch_or_iter(config):
+    cfg = Config.fromfile('./configs/' + config)
+    if cfg.train_cfg.type == 'EpochBasedTrainLoop':
+        epoch = cfg.train_cfg.max_epochs
+        return epoch
+    else:
+        return cfg.runner.max_iters
+
+
+def get_final_results(log_json_path,
+                      epoch_or_iter,
+                      results_lut='coco/bbox_mAP',
+                      by_epoch=True):
+    result_dict = dict()
+    with open(log_json_path) as f:
+        r = f.readlines()[-1]
+        last_metric = r.split(',')[0].split(': ')[-1].strip()
+    result_dict[results_lut] = last_metric
+    return result_dict
+
+
+def get_dataset_name(config):
+    # If there are more dataset, add here.
+    name_map = dict(
+        CityscapesDataset='Cityscapes',
+        CocoDataset='COCO',
+        PoseCocoDataset='COCO Person',
+        YOLOv5CocoDataset='COCO',
+        CocoPanopticDataset='COCO',
+        YOLOv5DOTADataset='DOTA 1.0',
+        DeepFashionDataset='Deep Fashion',
+        LVISV05Dataset='LVIS v0.5',
+        LVISV1Dataset='LVIS v1',
+        VOCDataset='Pascal VOC',
+        YOLOv5VOCDataset='Pascal VOC',
+        WIDERFaceDataset='WIDER Face',
+        OpenImagesDataset='OpenImagesDataset',
+        OpenImagesChallengeDataset='OpenImagesChallengeDataset')
+    cfg = Config.fromfile('./configs/' + config)
+    return name_map[cfg.dataset_type]
+
+
+def find_last_dir(model_dir):
+    dst_times = []
+    for time_stamp in os.scandir(model_dir):
+        if osp.isdir(time_stamp):
+            dst_time = time.mktime(
+                time.strptime(time_stamp.name, '%Y%m%d_%H%M%S'))
+            dst_times.append([dst_time, time_stamp.name])
+    return max(dst_times, key=lambda x: x[0])[1]
+
+
+def convert_model_info_to_pwc(model_infos):
+    pwc_files = {}
+    for model in model_infos:
+        cfg_folder_name = osp.split(model['config'])[-2]
+        pwc_model_info = OrderedDict()
+        pwc_model_info['Name'] = osp.split(model['config'])[-1].split('.')[0]
+        pwc_model_info['In Collection'] = 'Please fill in Collection name'
+        pwc_model_info['Config'] = osp.join('configs', model['config'])
+
+        # get metadata
+        meta_data = OrderedDict()
+        if 'epochs' in model:
+            meta_data['Epochs'] = get_real_epoch_or_iter(model['config'])
+        else:
+            meta_data['Iterations'] = get_real_epoch_or_iter(model['config'])
+        pwc_model_info['Metadata'] = meta_data
+
+        # get dataset name
+        dataset_name = get_dataset_name(model['config'])
+
+        # get results
+        results = []
+        # if there are more metrics, add here.
+        if 'bbox_mAP' in model['results']:
+            metric = round(model['results']['bbox_mAP'] * 100, 1)
+            results.append(
+                OrderedDict(
+                    Task='Object Detection',
+                    Dataset=dataset_name,
+                    Metrics={'box AP': metric}))
+        if 'segm_mAP' in model['results']:
+            metric = round(model['results']['segm_mAP'] * 100, 1)
+            results.append(
+                OrderedDict(
+                    Task='Instance Segmentation',
+                    Dataset=dataset_name,
+                    Metrics={'mask AP': metric}))
+        if 'PQ' in model['results']:
+            metric = round(model['results']['PQ'], 1)
+            results.append(
+                OrderedDict(
+                    Task='Panoptic Segmentation',
+                    Dataset=dataset_name,
+                    Metrics={'PQ': metric}))
+        pwc_model_info['Results'] = results
+
+        link_string = 'https://download.openmmlab.com/mmyolo/v0/'
+        link_string += '{}/{}'.format(model['config'].rstrip('.py'),
+                                      osp.split(model['model_path'])[-1])
+        pwc_model_info['Weights'] = link_string
+        if cfg_folder_name in pwc_files:
+            pwc_files[cfg_folder_name].append(pwc_model_info)
+        else:
+            pwc_files[cfg_folder_name] = [pwc_model_info]
+    return pwc_files
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Gather benchmarked models')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        'out', type=str, help='output path of gathered models to be stored')
+    parser.add_argument(
+        '--best',
+        action='store_true',
+        help='whether to gather the best model.')
+
+    args = parser.parse_args()
+    return args
+
+
+# TODO: Refine
+def main():
+    args = parse_args()
+    models_root = args.root
+    models_out = args.out
+    mkdir_or_exist(models_out)
+
+    # find all models in the root directory to be gathered
+    raw_configs = list(scandir('./configs', '.py', recursive=True))
+
+    # filter configs that is not trained in the experiments dir
+    used_configs = []
+    for raw_config in raw_configs:
+        if osp.exists(osp.join(models_root, raw_config)):
+            used_configs.append(raw_config)
+    print(f'Find {len(used_configs)} models to be gathered')
+
+    # find final_ckpt and log file for trained each config
+    # and parse the best performance
+    model_infos = []
+    for used_config in used_configs:
+        exp_dir = osp.join(models_root, used_config)
+        by_epoch = is_by_epoch(used_config)
+        # check whether the exps is finished
+        if args.best is True:
+            final_model, final_epoch_or_iter = get_best_epoch_or_iter(exp_dir)
+        else:
+            final_epoch_or_iter = get_final_epoch_or_iter(used_config)
+            final_model = '{}_{}.pth'.format('epoch' if by_epoch else 'iter',
+                                             final_epoch_or_iter)
+
+        model_path = osp.join(exp_dir, final_model)
+        # skip if the model is still training
+        if not osp.exists(model_path):
+            continue
+
+        # get the latest logs
+        latest_exp_name = find_last_dir(exp_dir)
+        latest_exp_json = osp.join(exp_dir, latest_exp_name, 'vis_data',
+                                   latest_exp_name + '.json')
+
+        model_performance = get_final_results(
+            latest_exp_json, final_epoch_or_iter, by_epoch=by_epoch)
+
+        if model_performance is None:
+            continue
+
+        model_info = dict(
+            config=used_config,
+            results=model_performance,
+            final_model=final_model,
+            latest_exp_json=latest_exp_json,
+            latest_exp_name=latest_exp_name)
+        model_info['epochs' if by_epoch else 'iterations'] = \
+            final_epoch_or_iter
+        model_infos.append(model_info)
+
+    # publish model for each checkpoint
+    publish_model_infos = []
+    for model in model_infos:
+        model_publish_dir = osp.join(models_out, model['config'].rstrip('.py'))
+        mkdir_or_exist(model_publish_dir)
+
+        model_name = osp.split(model['config'])[-1].split('.')[0]
+
+        model_name += '_' + model['latest_exp_name']
+        publish_model_path = osp.join(model_publish_dir, model_name)
+        trained_model_path = osp.join(models_root, model['config'],
+                                      model['final_model'])
+
+        # convert model
+        final_model_path = process_checkpoint(trained_model_path,
+                                              publish_model_path)
+
+        # copy log
+        shutil.copy(model['latest_exp_json'],
+                    osp.join(model_publish_dir, f'{model_name}.log.json'))
+
+        # copy config to guarantee reproducibility
+        config_path = model['config']
+        config_path = osp.join(
+            'configs',
+            config_path) if 'configs' not in config_path else config_path
+        target_config_path = osp.split(config_path)[-1]
+        shutil.copy(config_path, osp.join(model_publish_dir,
+                                          target_config_path))
+
+        model['model_path'] = final_model_path
+        publish_model_infos.append(model)
+
+    models = dict(models=publish_model_infos)
+    print(f'Totally gathered {len(publish_model_infos)} models')
+    dump(models, osp.join(models_out, 'model_info.json'))
+
+    pwc_files = convert_model_info_to_pwc(publish_model_infos)
+    for name in pwc_files:
+        with open(osp.join(models_out, name + '_metafile.yml'), 'w') as f:
+            ordered_yaml_dump(pwc_files[name], f, encoding='utf-8')
+
+
+if __name__ == '__main__':
+    main()

third_party/mmyolo/.dev_scripts/print_registers.py

@@ -0,0 +1,448 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import importlib
+import os
+import os.path as osp
+import pkgutil
+import sys
+import tempfile
+from multiprocessing import Pool
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+# host_addr = 'https://gitee.com/open-mmlab'
+host_addr = 'https://github.com/open-mmlab'
+tools_list = ['tools', '.dev_scripts']
+proxy_names = {
+    'mmdet': 'mmdetection',
+    'mmseg': 'mmsegmentation',
+    'mmcls': 'mmclassification'
+}
+merge_module_keys = {'mmcv': ['mmengine']}
+# exclude_prefix = {'mmcv': ['<class \'mmengine.model.']}
+exclude_prefix = {}
+markdown_title = '# MM 系列开源库注册表\n'
+markdown_title += '（注意：本文档是通过 .dev_scripts/print_registers.py 脚本自动生成）'
+
+
+def capitalize(repo_name):
+    lower = repo_name.lower()
+    if lower == 'mmcv':
+        return repo_name.upper()
+    elif lower.startswith('mm'):
+        return 'MM' + repo_name[2:]
+    return repo_name.capitalize()
+
+
+def mkdir_or_exist(dir_name, mode=0o777):
+    if dir_name == '':
+        return
+    dir_name = osp.expanduser(dir_name)
+    os.makedirs(dir_name, mode=mode, exist_ok=True)
+
+
+def parse_repo_name(repo_name):
+    proxy_names_rev = dict(zip(proxy_names.values(), proxy_names.keys()))
+    repo_name = proxy_names.get(repo_name, repo_name)
+    module_name = proxy_names_rev.get(repo_name, repo_name)
+    return repo_name, module_name
+
+
+def git_pull_branch(repo_name, branch_name='', pulldir='.'):
+    mkdir_or_exist(pulldir)
+    exec_str = f'cd {pulldir};git init;git pull '
+    exec_str += f'{host_addr}/{repo_name}.git'
+    if branch_name:
+        exec_str += f' {branch_name}'
+    returncode = os.system(exec_str)
+    if returncode:
+        raise RuntimeError(
+            f'failed to get the remote repo, code: {returncode}')
+
+
+def load_modules_from_dir(module_name, module_root, throw_error=False):
+    print(f'loading the {module_name} modules...')
+    # # install the dependencies
+    # if osp.exists(osp.join(pkg_dir, 'requirements.txt')):
+    #     os.system('pip install -r requirements.txt')
+    # get all module list
+    module_list = []
+    error_dict = {}
+    module_root = osp.join(module_root, module_name)
+    assert osp.exists(module_root), \
+        f'cannot find the module root: {module_root}'
+    for _root, _dirs, _files in os.walk(module_root):
+        if (('__init__.py' not in _files)
+                and (osp.split(_root)[1] != '__pycache__')):
+            # add __init__.py file to the package
+            with open(osp.join(_root, '__init__.py'), 'w') as _:
+                pass
+
+    def _onerror(*args, **kwargs):
+        pass
+
+    for _finder, _name, _ispkg in pkgutil.walk_packages([module_root],
+                                                        prefix=module_name +
+                                                        '.',
+                                                        onerror=_onerror):
+        try:
+            module = importlib.import_module(_name)
+            module_list.append(module)
+        except Exception as e:
+            if throw_error:
+                raise e
+            _error_msg = f'{type(e)}: {e}.'
+            print(f'cannot import the module: {_name} ({_error_msg})')
+            assert (_name not in error_dict), \
+                f'duplicate error name was found: {_name}'
+            error_dict[_name] = _error_msg
+    for module in module_list:
+        assert module.__file__.startswith(module_root), \
+            f'the importing path of package was wrong: {module.__file__}'
+    print('modules were loaded...')
+    return module_list, error_dict
+
+
+def get_registries_from_modules(module_list):
+    registries = {}
+    objects_set = set()
+    # import the Registry class,
+    # import at the beginning is not allowed
+    # because it is not the temp package
+    from mmengine.registry import Registry
+
+    # only get the specific registries in module list
+    for module in module_list:
+        for obj_name in dir(module):
+            _obj = getattr(module, obj_name)
+            if isinstance(_obj, Registry):
+                objects_set.add(_obj)
+    for _obj in objects_set:
+        if _obj.scope not in registries:
+            registries[_obj.scope] = {}
+        registries_scope = registries[_obj.scope]
+        assert _obj.name not in registries_scope, \
+            f'multiple definition of {_obj.name} in registries'
+        registries_scope[_obj.name] = {
+            key: str(val)
+            for key, val in _obj.module_dict.items()
+        }
+    print('registries got...')
+    return registries
+
+
+def merge_registries(src_dict, dst_dict):
+    assert type(src_dict) == type(dst_dict), \
+        (f'merge type is not supported: '
+         f'{type(dst_dict)} and {type(src_dict)}')
+    if isinstance(src_dict, str):
+        return
+    for _k, _v in dst_dict.items():
+        if (_k not in src_dict):
+            src_dict.update({_k: _v})
+        else:
+            assert isinstance(_v, (dict, str)) and \
+                isinstance(src_dict[_k], (dict, str)), \
+                'merge type is not supported: ' \
+                f'{type(_v)} and {type(src_dict[_k])}'
+            merge_registries(src_dict[_k], _v)
+
+
+def exclude_registries(registries, exclude_key):
+    for _k in list(registries.keys()):
+        _v = registries[_k]
+        if isinstance(_v, str) and _v.startswith(exclude_key):
+            registries.pop(_k)
+        elif isinstance(_v, dict):
+            exclude_registries(_v, exclude_key)
+
+
+def get_scripts_from_dir(root):
+
+    def _recurse(_dict, _chain):
+        if len(_chain) <= 1:
+            _dict[_chain[0]] = None
+            return
+        _key, *_chain = _chain
+        if _key not in _dict:
+            _dict[_key] = {}
+        _recurse(_dict[_key], _chain)
+
+    # find all scripts in the root directory. (not just ('.py', '.sh'))
+    # can not use the scandir function in mmengine to scan the dir,
+    # because mmengine import is not allowed before git pull
+    scripts = {}
+    for _subroot, _dirs, _files in os.walk(root):
+        for _file in _files:
+            _script = osp.join(osp.relpath(_subroot, root), _file)
+            _recurse(scripts, Path(_script).parts)
+    return scripts
+
+
+def get_version_from_module_name(module_name, branch):
+    branch_str = str(branch) if branch is not None else ''
+    version_str = ''
+    try:
+        exec(f'import {module_name}')
+        _module = eval(f'{module_name}')
+        if hasattr(_module, '__version__'):
+            version_str = str(_module.__version__)
+        else:
+            version_str = branch_str
+        version_str = f' ({version_str})' if version_str else version_str
+    except (ImportError, AttributeError) as e:
+        print(f'can not get the version of module {module_name}: {e}')
+    return version_str
+
+
+def print_tree(print_dict):
+    # recursive print the dict tree
+    def _recurse(_dict, _connector='', n=0):
+        assert isinstance(_dict, dict), 'recursive type must be dict'
+        tree = ''
+        for idx, (_key, _val) in enumerate(_dict.items()):
+            sub_tree = ''
+            _last = (idx == (len(_dict) - 1))
+            if isinstance(_val, str):
+                _key += f' ({_val})'
+            elif isinstance(_val, dict):
+                sub_tree = _recurse(_val,
+                                    _connector + ('   ' if _last else '│  '),
+                                    n + 1)
+            else:
+                assert (_val is None), f'unknown print type {_val}'
+            tree += '  ' + _connector + \
+                    ('└─' if _last else '├─') + f'({n}) {_key}' + '\n'
+            tree += sub_tree
+        return tree
+
+    for _pname, _pdict in print_dict.items():
+        print('-' * 100)
+        print(f'{_pname}\n' + _recurse(_pdict))
+
+
+def divide_list_into_groups(_array, _maxsize_per_group):
+    if not _array:
+        return _array
+    _groups = np.asarray(len(_array) / _maxsize_per_group)
+    if len(_array) % _maxsize_per_group:
+        _groups = np.floor(_groups) + 1
+    _groups = _groups.astype(int)
+    return np.array_split(_array, _groups)
+
+
+def registries_to_html(registries, title=''):
+    max_col_per_row = 5
+    max_size_per_cell = 20
+    html = ''
+    table_data = []
+    # save repository registries
+    for registry_name, registry_dict in registries.items():
+        # filter the empty registries
+        if not registry_dict:
+            continue
+        registry_strings = []
+        if isinstance(registry_dict, dict):
+            registry_dict = list(registry_dict.keys())
+        elif isinstance(registry_dict, list):
+            pass
+        else:
+            raise TypeError(
+                f'unknown type of registry_dict {type(registry_dict)}')
+        for _k in registry_dict:
+            registry_strings.append(f'<li>{_k}</li>')
+        table_data.append((registry_name, registry_strings))
+
+    # sort the data list
+    table_data = sorted(table_data, key=lambda x: len(x[1]))
+    # split multi parts
+    table_data_multi_parts = []
+    for (registry_name, registry_strings) in table_data:
+        multi_parts = False
+        if len(registry_strings) > max_size_per_cell:
+            multi_parts = True
+        for cell_idx, registry_cell in enumerate(
+                divide_list_into_groups(registry_strings, max_size_per_cell)):
+            registry_str = ''.join(registry_cell.tolist())
+            registry_str = f'<ul>{registry_str}</ul>'
+            table_data_multi_parts.append([
+                registry_name if not multi_parts else
+                f'{registry_name} (part {cell_idx + 1})', registry_str
+            ])
+
+    for table_data in divide_list_into_groups(table_data_multi_parts,
+                                              max_col_per_row):
+        table_data = list(zip(*table_data.tolist()))
+        html += dataframe_to_html(
+            pd.DataFrame([table_data[1]], columns=table_data[0]))
+    if html:
+        html = f'<div align=\'center\'><b>{title}</b></div>\n{html}'
+        html = f'<details open>{html}</details>\n'
+    return html
+
+
+def tools_to_html(tools_dict, repo_name=''):
+
+    def _recurse(_dict, _connector, _result):
+        assert isinstance(_dict, dict), \
+            f'unknown recurse type: {_dict} ({type(_dict)})'
+        for _k, _v in _dict.items():
+            if _v is None:
+                if _connector not in _result:
+                    _result[_connector] = []
+                _result[_connector].append(_k)
+            else:
+                _recurse(_v, osp.join(_connector, _k), _result)
+
+    table_data = {}
+    title = f'{capitalize(repo_name)} Tools'
+    _recurse(tools_dict, '', table_data)
+    return registries_to_html(table_data, title)
+
+
+def dataframe_to_html(dataframe):
+    styler = dataframe.style
+    styler = styler.hide(axis='index')
+    styler = styler.format(na_rep='-')
+    styler = styler.set_properties(**{
+        'text-align': 'left',
+        'align': 'center',
+        'vertical-align': 'top'
+    })
+    styler = styler.set_table_styles([{
+        'selector':
+        'thead th',
+        'props':
+        'align:center;text-align:center;vertical-align:bottom'
+    }])
+    html = styler.to_html()
+    html = f'<div align=\'center\'>\n{html}</div>'
+    return html
+
+
+def generate_markdown_by_repository(repo_name,
+                                    module_name,
+                                    branch,
+                                    pulldir,
+                                    throw_error=False):
+    # add the pull dir to the system path so that it can be found
+    if pulldir not in sys.path:
+        sys.path.insert(0, pulldir)
+    module_list, error_dict = load_modules_from_dir(
+        module_name, pulldir, throw_error=throw_error)
+    registries_tree = get_registries_from_modules(module_list)
+    if error_dict:
+        error_dict_name = 'error_modules'
+        assert (error_dict_name not in registries_tree), \
+            f'duplicate module name was found: {error_dict_name}'
+        registries_tree.update({error_dict_name: error_dict})
+    # get the tools files
+    for tools_name in tools_list:
+        assert (tools_name not in registries_tree), \
+            f'duplicate tools name was found: {tools_name}'
+        tools_tree = osp.join(pulldir, tools_name)
+        tools_tree = get_scripts_from_dir(tools_tree)
+        registries_tree.update({tools_name: tools_tree})
+    # print_tree(registries_tree)
+    # get registries markdown string
+    module_registries = registries_tree.get(module_name, {})
+    for merge_key in merge_module_keys.get(module_name, []):
+        merge_dict = registries_tree.get(merge_key, {})
+        merge_registries(module_registries, merge_dict)
+    for exclude_key in exclude_prefix.get(module_name, []):
+        exclude_registries(module_registries, exclude_key)
+    markdown_str = registries_to_html(
+        module_registries, title=f'{capitalize(repo_name)} Module Components')
+    # get tools markdown string
+    tools_registries = {}
+    for tools_name in tools_list:
+        tools_registries.update(
+            {tools_name: registries_tree.get(tools_name, {})})
+    markdown_str += tools_to_html(tools_registries, repo_name=repo_name)
+    version_str = get_version_from_module_name(module_name, branch)
+    title_str = f'\n\n## {capitalize(repo_name)}{version_str}\n'
+    # remove the pull dir from system path
+    if pulldir in sys.path:
+        sys.path.remove(pulldir)
+    return f'{title_str}{markdown_str}'
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='print registries in openmmlab repositories')
+    parser.add_argument(
+        '-r',
+        '--repositories',
+        nargs='+',
+        default=['mmdet', 'mmcls', 'mmseg', 'mmengine', 'mmcv'],
+        type=str,
+        help='git repositories name in OpenMMLab')
+    parser.add_argument(
+        '-b',
+        '--branches',
+        nargs='+',
+        default=['3.x', '1.x', '1.x', 'main', '2.x'],
+        type=str,
+        help='the branch names of git repositories, the length of branches '
+        'must be same as the length of repositories')
+    parser.add_argument(
+        '-o', '--out', type=str, default='.', help='output path of the file')
+    parser.add_argument(
+        '--throw-error',
+        action='store_true',
+        default=False,
+        help='whether to throw error when trying to import modules')
+    args = parser.parse_args()
+    return args
+
+
+# TODO: Refine
+def main():
+    args = parse_args()
+    repositories = args.repositories
+    branches = args.branches
+    assert isinstance(repositories, list), \
+        'Type of repositories must be list'
+    if branches is None:
+        branches = [None] * len(repositories)
+    assert isinstance(branches, list) and \
+           len(branches) == len(repositories), \
+           'The length of branches must be same as ' \
+           'that of repositories'
+    assert isinstance(args.out, str), \
+        'The type of output path must be string'
+    # save path of file
+    mkdir_or_exist(args.out)
+    save_path = osp.join(args.out, 'registries_info.md')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # multi process init
+        pool = Pool(processes=len(repositories))
+        multi_proc_input_list = []
+        multi_proc_output_list = []
+        # get the git repositories
+        for branch, repository in zip(branches, repositories):
+            repo_name, module_name = parse_repo_name(repository)
+            pulldir = osp.join(tmpdir, f'tmp_{repo_name}')
+            git_pull_branch(
+                repo_name=repo_name, branch_name=branch, pulldir=pulldir)
+            multi_proc_input_list.append(
+                (repo_name, module_name, branch, pulldir, args.throw_error))
+        print('starting the multi process to get the registries')
+        for multi_proc_input in multi_proc_input_list:
+            multi_proc_output_list.append(
+                pool.apply_async(generate_markdown_by_repository,
+                                 multi_proc_input))
+        pool.close()
+        pool.join()
+        with open(save_path, 'w', encoding='utf-8') as fw:
+            fw.write(f'{markdown_title}\n')
+            for multi_proc_output in multi_proc_output_list:
+                markdown_str = multi_proc_output.get()
+                fw.write(f'{markdown_str}\n')
+    print(f'saved registries to the path: {save_path}')
+
+
+if __name__ == '__main__':
+    main()

third_party/mmyolo/.github/CODE_OF_CONDUCT.md

@@ -0,0 +1,76 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+- Using welcoming and inclusive language
+- Being respectful of differing viewpoints and experiences
+- Gracefully accepting constructive criticism
+- Focusing on what is best for the community
+- Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+- The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+- Trolling, insulting/derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+- Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at chenkaidev@gmail.com. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq
+
+[homepage]: https://www.contributor-covenant.org

third_party/mmyolo/.github/CONTRIBUTING.md

@@ -0,0 +1 @@
+We appreciate all contributions to improve MMYOLO. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline.

third_party/mmyolo/.github/ISSUE_TEMPLATE/1-bug-report.yml

@@ -0,0 +1,67 @@
+name: "🐞 Bug report"
+description: "Create a report to help us reproduce and fix the bug"
+
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for reporting this issue to help us improve!
+        If you have already identified the reason, we strongly appreciate you creating a new PR to fix it [here](https://github.com/open-mmlab/mmyolo/pulls)!
+        If this issue is about installing MMCV, please file an issue at [MMCV](https://github.com/open-mmlab/mmcv/issues/new/choose).
+        If you need our help, please fill in as much of the following form as you're able.
+
+  - type: checkboxes
+    attributes:
+      label: Prerequisite
+      description: Please check the following items before creating a new issue.
+      options:
+      - label: I have searched [the existing and past issues](https://github.com/open-mmlab/mmyolo/issues) but cannot get the expected help.
+        required: true
+      - label: I have read the [FAQ documentation](https://mmyolo.readthedocs.io/en/latest/faq.html) but cannot get the expected help.
+        required: true
+      - label: The bug has not been fixed in the [latest version](https://github.com/open-mmlab/mmyolo).
+        required: true
+
+  - type: textarea
+    attributes:
+      label: 🐞 Describe the bug
+      description: |
+        Please provide a clear and concise description of what the bug is.
+        Preferably a simple and minimal code snippet that we can reproduce the error by running the code.
+      placeholder: |
+        A clear and concise description of what the bug is.
+
+        ```python
+        # Sample code to reproduce the problem
+        ```
+
+        ```shell
+        The command or script you run.
+        ```
+
+        ```
+        The error message or logs you got, with the full traceback.
+        ```
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Environment
+      description: |
+        Please run `python mmyolo/utils/collect_env.py` to collect necessary environment information and paste it here.
+        You may add addition that may be helpful for locating the problem, such as
+          - How you installed PyTorch \[e.g., pip, conda, source\]
+          - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Additional information
+      description: Tell us anything else you think we should know.
+      placeholder: |
+        1. Did you make any modifications on the code or config? Did you understand what you have modified?
+        2. What dataset did you use?
+        3. What do you think might be the reason?

third_party/mmyolo/.github/ISSUE_TEMPLATE/2-feature-request.yml

@@ -0,0 +1,32 @@
+name: 🚀 Feature request
+description: Suggest an idea for this project
+labels: [feature request]
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for suggesting an idea to make MMYOLO better.
+        We strongly appreciate you creating a PR to implete this feature [here](https://github.com/open-mmlab/mmyolo/pulls)!
+
+        If you need our help, please fill in as much of the following form as you're able.
+
+  - type: textarea
+    attributes:
+      label: What is the problem this feature will solve?
+      placeholder: |
+        E.g., It is inconvenient when \[....\].
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: What is the feature you are proposing to solve the problem?
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: What alternatives have you considered?
+      description: |
+        Add any other context or screenshots about the feature request here.

third_party/mmyolo/.github/ISSUE_TEMPLATE/3-new-model.yml

@@ -0,0 +1,30 @@
+name: "\U0001F31F New model/dataset addition"
+description: Submit a proposal/request to implement a new model / dataset
+labels: [ "New model/dataset" ]
+
+body:
+  - type: textarea
+    id: description-request
+    validations:
+      required: true
+    attributes:
+      label: Model/Dataset description
+      description: |
+        Put any and all important information relative to the model/dataset
+
+  - type: checkboxes
+    attributes:
+      label: Open source status
+      description: |
+          Please provide the open-source status, which would be very helpful
+      options:
+        - label: "The model implementation is available"
+        - label: "The model weights are available."
+
+  - type: textarea
+    id: additional-info
+    attributes:
+      label: Provide useful links for the implementation
+      description: |
+        Please provide information regarding the implementation, the weights, and the authors.
+        Please mention the authors by @gh-username if you're aware of their usernames.

third_party/mmyolo/.github/ISSUE_TEMPLATE/4-documentation.yml

@@ -0,0 +1,22 @@
+name: 📚 Documentation
+description: Report an issue related to https://mmyolo.readthedocs.io/en/latest/.
+
+body:
+- type: textarea
+  attributes:
+    label: 📚 The doc issue
+    description: >
+      A clear and concise description of what content in https://mmyolo.readthedocs.io/en/latest/ is an issue.
+  validations:
+    required: true
+
+- type: textarea
+  attributes:
+    label: Suggest a potential alternative/fix
+    description: >
+      Tell us how we could improve the documentation in this regard.
+
+- type: markdown
+  attributes:
+    value: >
+      Thanks for contributing 🎉!

third_party/mmyolo/.github/ISSUE_TEMPLATE/5-reimplementation.yml

@@ -0,0 +1,87 @@
+name: "💥 Reimplementation Questions"
+description: "Ask about questions during model reimplementation"
+
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        If you have already identified the reason, we strongly appreciate you creating a new PR to fix it [here](https://github.com/open-mmlab/mmyolo/pulls)!
+
+  - type: checkboxes
+    attributes:
+      label: Prerequisite
+      description: Please check the following items before creating a new issue.
+      options:
+      - label: I have searched [the existing and past issues](https://github.com/open-mmlab/mmyolo/issues) but cannot get the expected help.
+        required: true
+      - label: I have read the [FAQ documentation](https://mmyolo.readthedocs.io/en/latest/faq.html) but cannot get the expected help.
+        required: true
+      - label: The bug has not been fixed in the [latest version](https://github.com/open-mmlab/mmyolo).
+        required: true
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: 💬 Describe the reimplementation questions
+      description: |
+        A clear and concise description of what the problem you meet and what have you done.
+        There are several common situations in the reimplementation issues as below
+
+        1. Reimplement a model in the model zoo using the provided configs
+        2. Reimplement a model in the model zoo on other dataset (e.g., custom datasets)
+        3. Reimplement a custom model but all the components are implemented in MMDetection
+        4. Reimplement a custom model with new modules implemented by yourself
+
+        There are several things to do for different cases as below.
+
+        - For case 1 & 3, please follow the steps in the following sections thus we could help to quick identify the issue.
+        - For case 2 & 4, please understand that we are not able to do much help here because we usually do not know the full code and the users should be responsible to the code they write.
+        - One suggestion for case 2 & 4 is that the users should first check whether the bug lies in the self-implemented code or the original code. For example, users can first make sure that the same model runs well on supported datasets. If you still need help, please describe what you have done and what you obtain in the issue, and follow the steps in the following sections and try as clear as possible so that we can better help you.
+      placeholder: |
+        A clear and concise description of what the bug is.
+        What config dir you run?
+
+        ```none
+        A placeholder for the config.
+        ```
+
+        ```shell
+        The command or script you run.
+        ```
+
+        ```
+        The error message or logs you got, with the full traceback.
+        ```
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Environment
+      description: |
+         Please run `python mmyolo/utils/collect_env.py` to collect necessary environment information and paste it here.
+         You may add addition that may be helpful for locating the problem, such as
+            - How you installed PyTorch \[e.g., pip, conda, source\]
+            - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Expected results
+      description: If applicable, paste the related results here, e.g., what you expect and what you get.
+      placeholder: |
+         ```none
+         A placeholder for results comparison
+         ```
+
+  - type: textarea
+    attributes:
+      label: Additional information
+      description: Tell us anything else you think we should know.
+      placeholder: |
+        1. Did you make any modifications on the code or config? Did you understand what you have modified?
+        2. What dataset did you use?
+        3. What do you think might be the reason?

third_party/mmyolo/.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,9 @@
+blank_issues_enabled: true
+
+contact_links:
+  - name: 💬 Forum
+    url: https://github.com/open-mmlab/mmyolo/discussions
+    about: Ask general usage questions and discuss with other MMYOLO community members
+  - name: 🌐 Explore OpenMMLab
+    url: https://openmmlab.com/
+    about: Get know more about OpenMMLab

third_party/mmyolo/.github/pull_request_template.md

@@ -0,0 +1,25 @@
+Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
+
+## Motivation
+
+Please describe the motivation for this PR and the goal you want to achieve through this PR.
+
+## Modification
+
+Please briefly describe what modification is made in this PR.
+
+## BC-breaking (Optional)
+
+Does the modification introduce changes that break the backward compatibility of the downstream repos?
+If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.
+
+## Use cases (Optional)
+
+If this PR introduces a new feature, it is better to list some use cases here and update the documentation.
+
+## Checklist
+
+1. Pre-commit or other linting tools are used to fix potential lint issues.
+2. The modification is covered by complete unit tests. If not, please add more unit tests to ensure the correctness.
+3. If the modification has a potential influence on downstream projects, this PR should be tested with downstream projects, like MMDetection or MMClassification.
+4. The documentation has been modified accordingly, like docstring or example tutorials.

third_party/mmyolo/.github/workflows/deploy.yml

@@ -0,0 +1,28 @@
+name: deploy
+
+on: push
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  build-n-publish:
+    runs-on: ubuntu-latest
+    if: startsWith(github.event.ref, 'refs/tags')
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      - name: Install torch
+        run: pip install torch
+      - name: Install wheel
+        run: pip install wheel
+      - name: Build MMYOLO
+        run: python setup.py sdist bdist_wheel
+      - name: Publish distribution to PyPI
+        run: |
+          pip install twine
+          twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }}

third_party/mmyolo/.gitignore

@@ -0,0 +1,126 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/en/_build/
+docs/zh_cn/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+data/
+data
+.vscode
+.idea
+.DS_Store
+
+# custom
+*.pkl
+*.pkl.json
+*.log.json
+docs/modelzoo_statistics.md
+mmyolo/.mim
+output/
+work_dirs
+yolov5-6.1/
+
+# Pytorch
+*.pth
+*.pt
+*.py~
+*.sh~

third_party/mmyolo/.pre-commit-config-zh-cn.yaml

@@ -0,0 +1,60 @@
+exclude: ^tests/data/
+repos:
+  - repo: https://gitee.com/openmmlab/mirrors-flake8
+    rev: 5.0.4
+    hooks:
+      - id: flake8
+  - repo: https://gitee.com/openmmlab/mirrors-isort
+    rev: 5.11.5
+    hooks:
+      - id: isort
+  - repo: https://gitee.com/openmmlab/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+  - repo: https://gitee.com/openmmlab/mirrors-pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://gitee.com/openmmlab/mirrors-mdformat
+    rev: 0.7.9
+    hooks:
+      - id: mdformat
+        args: ["--number"]
+        additional_dependencies:
+          - mdformat-openmmlab
+          - mdformat_frontmatter
+          - linkify-it-py
+  - repo: https://gitee.com/openmmlab/mirrors-codespell
+    rev: v2.2.1
+    hooks:
+      - id: codespell
+  - repo: https://gitee.com/openmmlab/mirrors-docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://gitee.com/openmmlab/mirrors-pyupgrade
+    rev: v3.0.0
+    hooks:
+      - id: pyupgrade
+        args: ["--py36-plus"]
+  - repo: https://github.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0
+    hooks:
+      - id: check-copyright
+        args: ["mmyolo", "tests"]
+#  - repo: https://gitee.com/openmmlab/mirrors-mypy
+#    rev: v0.812
+#    hooks:
+#      - id: mypy
+#        exclude: "docs"

third_party/mmyolo/.pre-commit-config.yaml

@@ -0,0 +1,60 @@
+exclude: ^tests/data/
+repos:
+  - repo: https://github.com/PyCQA/flake8
+    rev: 5.0.4
+    hooks:
+      - id: flake8
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.11.5
+    hooks:
+      - id: isort
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.9
+    hooks:
+      - id: mdformat
+        args: ["--number"]
+        additional_dependencies:
+          - mdformat-openmmlab
+          - mdformat_frontmatter
+          - linkify-it-py
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.1
+    hooks:
+      - id: codespell
+  - repo: https://github.com/myint/docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.0.0
+    hooks:
+      - id: pyupgrade
+        args: ["--py36-plus"]
+  - repo: https://github.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0
+    hooks:
+      - id: check-copyright
+        args: ["mmyolo", "tests"]
+#  - repo: https://github.com/pre-commit/mirrors-mypy
+#    rev: v0.812
+#    hooks:
+#      - id: mypy
+#        exclude: "docs"

third_party/mmyolo/.readthedocs.yml

@@ -0,0 +1,8 @@
+version: 2
+
+formats: all
+
+python:
+  version: 3.7
+  install:
+    - requirements: requirements/docs.txt

third_party/mmyolo/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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third_party/mmyolo/MANIFEST.in

@@ -0,0 +1,6 @@
+include requirements/*.txt
+include mmyolo/VERSION
+include mmyolo/.mim/model-index.yml
+include mmyolo/.mim/demo/*/*
+recursive-include mmyolo/.mim/configs *.py *.yml
+recursive-include mmyolo/.mim/tools *.sh *.py

third_party/mmyolo/README.md

@@ -0,0 +1,428 @@
+<div align="center">
+  <img width="100%" src="https://user-images.githubusercontent.com/27466624/222385101-516e551c-49f5-480d-a135-4b24ee6dc308.png"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">OpenMMLab website</font></b>
+    <sup>
+      <a href="https://openmmlab.com">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <b><font size="5">OpenMMLab platform</font></b>
+    <sup>
+      <a href="https://platform.openmmlab.com">
+        <i><font size="4">TRY IT OUT</font></i>
+      </a>
+    </sup>
+  </div>
+  <div>&nbsp;</div>
+
+[![PyPI](https://img.shields.io/pypi/v/mmyolo)](https://pypi.org/project/mmyolo)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmyolo.readthedocs.io/en/latest/)
+[![deploy](https://github.com/open-mmlab/mmyolo/workflows/deploy/badge.svg)](https://github.com/open-mmlab/mmyolo/actions)
+[![codecov](https://codecov.io/gh/open-mmlab/mmyolo/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmyolo)
+[![license](https://img.shields.io/github/license/open-mmlab/mmyolo.svg)](https://github.com/open-mmlab/mmyolo/blob/main/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmyolo.svg)](https://github.com/open-mmlab/mmyolo/issues)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmyolo.svg)](https://github.com/open-mmlab/mmyolo/issues)
+
+[📘Documentation](https://mmyolo.readthedocs.io/en/latest/) |
+[🛠️Installation](https://mmyolo.readthedocs.io/en/latest/get_started/installation.html) |
+[👀Model Zoo](https://mmyolo.readthedocs.io/en/latest/model_zoo.html) |
+[🆕Update News](https://mmyolo.readthedocs.io/en/latest/notes/changelog.html) |
+[🤔Reporting Issues](https://github.com/open-mmlab/mmyolo/issues/new/choose)
+
+</div>
+
+<div align="center">
+
+English | [简体中文](README_zh-CN.md)
+
+</div>
+
+<div align="center">
+  <a href="https://openmmlab.medium.com/" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://discord.com/channels/1037617289144569886/1046608014234370059" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
+</div>
+
+## 📄 Table of Contents
+
+- [🥳 🚀 What's New](#--whats-new-)
+  - [✨ Highlight](#-highlight-)
+- [📖 Introduction](#-introduction-)
+- [🛠️ Installation](#%EF%B8%8F-installation-)
+- [👨‍🏫 Tutorial](#-tutorial-)
+- [📊 Overview of Benchmark and Model Zoo](#-overview-of-benchmark-and-model-zoo-)
+- [❓ FAQ](#-faq-)
+- [🙌 Contributing](#-contributing-)
+- [🤝 Acknowledgement](#-acknowledgement-)
+- [🖊️ Citation](#️-citation-)
+- [🎫 License](#-license-)
+- [🏗️ Projects in OpenMMLab](#%EF%B8%8F-projects-in-openmmlab-)
+
+## 🥳 🚀 What's New [🔝](#-table-of-contents)
+
+💎 **v0.6.0** was released on 15/8/2023:
+
+- Support YOLOv5 instance segmentation
+- Support YOLOX-Pose based on MMPose
+- Add 15 minutes instance segmentation tutorial.
+- YOLOv5 supports using mask annotation to optimize bbox
+- Add Multi-scale training and testing docs
+
+For release history and update details, please refer to [changelog](https://mmyolo.readthedocs.io/en/latest/notes/changelog.html).
+
+### ✨ Highlight [🔝](#-table-of-contents)
+
+We are excited to announce our latest work on real-time object recognition tasks, **RTMDet**, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the [technical report](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](configs/rtmdet).
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/real-time-instance-segmentation-on-mscoco)](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-dota-1)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-hrsc2016)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
+
+| Task                     | Dataset | AP                                   | FPS(TRT FP16 BS1 3090) |
+| ------------------------ | ------- | ------------------------------------ | ---------------------- |
+| Object Detection         | COCO    | 52.8                                 | 322                    |
+| Instance Segmentation    | COCO    | 44.6                                 | 188                    |
+| Rotated Object Detection | DOTA    | 78.9(single-scale)/81.3(multi-scale) | 121                    |
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
+</div>
+
+MMYOLO currently implements the object detection and rotated object detection algorithm, but it has a significant training acceleration compared to the MMDeteciton version. The training speed is 2.6 times faster than the previous version.
+
+## 📖 Introduction [🔝](#-table-of-contents)
+
+MMYOLO is an open source toolbox for YOLO series algorithms based on PyTorch and [MMDetection](https://github.com/open-mmlab/mmdetection). It is a part of the [OpenMMLab](https://openmmlab.com/) project.
+
+The master branch works with **PyTorch 1.6+**.
+<img src="https://user-images.githubusercontent.com/45811724/190993591-bd3f1f11-1c30-4b93-b5f4-05c9ff64ff7f.gif"/>
+
+<details open>
+<summary>Major features</summary>
+
+- 🕹️ **Unified and convenient benchmark**
+
+  MMYOLO unifies the implementation of modules in various YOLO algorithms and provides a unified benchmark. Users can compare and analyze in a fair and convenient way.
+
+- 📚 **Rich and detailed documentation**
+
+  MMYOLO provides rich documentation for getting started, model deployment, advanced usages, and algorithm analysis, making it easy for users at different levels to get started and make extensions quickly.
+
+- 🧩 **Modular Design**
+
+  MMYOLO decomposes the framework into different components where users can easily customize a model by combining different modules with various training and testing strategies.
+
+<img src="https://user-images.githubusercontent.com/27466624/199999337-0544a4cb-3cbd-4f3e-be26-bcd9e74db7ff.jpg" alt="BaseModule-P5"/>
+  The figure above is contributed by RangeKing@GitHub, thank you very much!
+
+And the figure of P6 model is in [model_design.md](docs/en/recommended_topics/model_design.md).
+
+</details>
+
+## 🛠️ Installation [🔝](#-table-of-contents)
+
+MMYOLO relies on PyTorch, MMCV, MMEngine, and MMDetection. Below are quick steps for installation. Please refer to the [Install Guide](docs/en/get_started/installation.md) for more detailed instructions.
+
+```shell
+conda create -n mmyolo python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
+conda activate mmyolo
+pip install openmim
+mim install "mmengine>=0.6.0"
+mim install "mmcv>=2.0.0rc4,<2.1.0"
+mim install "mmdet>=3.0.0,<4.0.0"
+git clone https://github.com/open-mmlab/mmyolo.git
+cd mmyolo
+# Install albumentations
+pip install -r requirements/albu.txt
+# Install MMYOLO
+mim install -v -e .
+```
+
+## 👨‍🏫 Tutorial [🔝](#-table-of-contents)
+
+MMYOLO is based on MMDetection and adopts the same code structure and design approach. To get better use of this, please read [MMDetection Overview](https://mmdetection.readthedocs.io/en/latest/get_started.html) for the first understanding of MMDetection.
+
+The usage of MMYOLO is almost identical to MMDetection and all tutorials are straightforward to use, you can also learn about [MMDetection User Guide and Advanced Guide](https://mmdetection.readthedocs.io/en/3.x/).
+
+For different parts from MMDetection, we have also prepared user guides and advanced guides, please read our [documentation](https://mmyolo.readthedocs.io/zenh_CN/latest/).
+
+<details>
+<summary>Get Started</summary>
+
+- [Overview](docs/en/get_started/overview.md)
+- [Dependencies](docs/en/get_started/dependencies.md)
+- [Installation](docs/en/get_started/installation.md)
+- [15 minutes object detection](docs/en/get_started/15_minutes_object_detection.md)
+- [15 minutes rotated object detection](docs/en/get_started/15_minutes_rotated_object_detection.md)
+- [15 minutes instance segmentation](docs/en/get_started/15_minutes_instance_segmentation.md)
+- [Resources summary](docs/en/get_started/article.md)
+
+</details>
+
+<details>
+<summary>Recommended Topics</summary>
+
+- [How to contribute code to MMYOLO](docs/en/recommended_topics/contributing.md)
+- [Training testing tricks](docs/en/recommended_topics/training_testing_tricks.md)
+- [MMYOLO model design](docs/en/recommended_topics/model_design.md)
+- [Algorithm principles and implementation](docs/en/recommended_topics/algorithm_descriptions/)
+- [Replace the backbone network](docs/en/recommended_topics/replace_backbone.md)
+- [MMYOLO model complexity analysis](docs/en/recommended_topics/complexity_analysis.md)
+- [Annotation-to-deployment workflow for custom dataset](docs/en/recommended_topics/labeling_to_deployment_tutorials.md)
+- [Visualization](docs/en/recommended_topics/visualization.md)
+- [Model deployment](docs/en/recommended_topics/deploy/)
+- [Troubleshooting steps](docs/en/recommended_topics/troubleshooting_steps.md)
+- [MMYOLO application examples](docs/en/recommended_topics/application_examples/)
+- [MM series repo essential basics](docs/en/recommended_topics/mm_basics.md)
+- [Dataset preparation and description](docs/en/recommended_topics/dataset_preparation.md)
+
+</details>
+
+<details>
+<summary>Common Usage</summary>
+
+- [Resume training](docs/en/common_usage/resume_training.md)
+- [Enabling and disabling SyncBatchNorm](docs/en/common_usage/syncbn.md)
+- [Enabling AMP](docs/en/common_usage/amp_training.md)
+- [Multi-scale training and testing](docs/en/common_usage/ms_training_testing.md)
+- [TTA Related Notes](docs/en/common_usage/tta.md)
+- [Add plugins to the backbone network](docs/en/common_usage/plugins.md)
+- [Freeze layers](docs/en/common_usage/freeze_layers.md)
+- [Output model predictions](docs/en/common_usage/output_predictions.md)
+- [Set random seed](docs/en/common_usage/set_random_seed.md)
+- [Module combination](docs/en/common_usage/module_combination.md)
+- [Cross-library calls using mim](docs/en/common_usage/mim_usage.md)
+- [Apply multiple Necks](docs/en/common_usage/multi_necks.md)
+- [Specify specific device training or inference](docs/en/common_usage/specify_device.md)
+- [Single and multi-channel application examples](docs/en/common_usage/single_multi_channel_applications.md)
+
+</details>
+
+<details>
+<summary>Useful Tools</summary>
+
+- [Browse coco json](docs/en/useful_tools/browse_coco_json.md)
+- [Browse dataset](docs/en/useful_tools/browse_dataset.md)
+- [Print config](docs/en/useful_tools/print_config.md)
+- [Dataset analysis](docs/en/useful_tools/dataset_analysis.md)
+- [Optimize anchors](docs/en/useful_tools/optimize_anchors.md)
+- [Extract subcoco](docs/en/useful_tools/extract_subcoco.md)
+- [Visualization scheduler](docs/en/useful_tools/vis_scheduler.md)
+- [Dataset converters](docs/en/useful_tools/dataset_converters.md)
+- [Download dataset](docs/en/useful_tools/download_dataset.md)
+- [Log analysis](docs/en/useful_tools/log_analysis.md)
+- [Model converters](docs/en/useful_tools/model_converters.md)
+
+</details>
+
+<details>
+<summary>Basic Tutorials</summary>
+
+- [Learn about configs with YOLOv5](docs/en/tutorials/config.md)
+- [Data flow](docs/en/tutorials/data_flow.md)
+- [Rotated detection](docs/en/tutorials/rotated_detection.md)
+- [Custom Installation](docs/en/tutorials/custom_installation.md)
+- [Common Warning Notes](docs/zh_cn/tutorials/warning_notes.md)
+- [FAQ](docs/en/tutorials/faq.md)
+
+</details>
+
+<details>
+<summary>Advanced Tutorials</summary>
+
+- [MMYOLO cross-library application](docs/en/advanced_guides/cross-library_application.md)
+
+</details>
+
+<details>
+<summary>Descriptions</summary>
+
+- [Changelog](docs/en/notes/changelog.md)
+- [Compatibility](docs/en/notes/compatibility.md)
+- [Conventions](docs/en/notes/conventions.md)
+- [Code Style](docs/en/notes/code_style.md)
+
+</details>
+
+## 📊 Overview of Benchmark and Model Zoo [🔝](#-table-of-contents)
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/17425982/222087414-168175cc-dae6-4c5c-a8e3-3109a152dd19.png"/>
+</div>
+
+Results and models are available in the [model zoo](docs/en/model_zoo.md).
+
+<details open>
+<summary><b>Supported Tasks</b></summary>
+
+- [x] Object detection
+- [x] Rotated object detection
+
+</details>
+
+<details open>
+<summary><b>Supported Algorithms</b></summary>
+
+- [x] [YOLOv5](configs/yolov5)
+- [ ] [YOLOv5u](configs/yolov5/yolov5u) (Inference only)
+- [x] [YOLOX](configs/yolox)
+- [x] [RTMDet](configs/rtmdet)
+- [x] [RTMDet-Rotated](configs/rtmdet)
+- [x] [YOLOv6](configs/yolov6)
+- [x] [YOLOv7](configs/yolov7)
+- [x] [PPYOLOE](configs/ppyoloe)
+- [x] [YOLOv8](configs/yolov8)
+
+</details>
+
+<details open>
+<summary><b>Supported Datasets</b></summary>
+
+- [x] COCO Dataset
+- [x] VOC Dataset
+- [x] CrowdHuman Dataset
+- [x] DOTA 1.0 Dataset
+
+</details>
+
+<details open>
+<div align="center">
+  <b>Module Components</b>
+</div>
+<table align="center">
+  <tbody>
+    <tr align="center" valign="bottom">
+      <td>
+        <b>Backbones</b>
+      </td>
+      <td>
+        <b>Necks</b>
+      </td>
+      <td>
+        <b>Loss</b>
+      </td>
+      <td>
+        <b>Common</b>
+      </td>
+    </tr>
+    <tr valign="top">
+      <td>
+      <ul>
+        <li>YOLOv5CSPDarknet</li>
+        <li>YOLOv8CSPDarknet</li>
+        <li>YOLOXCSPDarknet</li>
+        <li>EfficientRep</li>
+        <li>CSPNeXt</li>
+        <li>YOLOv7Backbone</li>
+        <li>PPYOLOECSPResNet</li>
+        <li>mmdet backbone</li>
+        <li>mmcls backbone</li>
+        <li>timm</li>
+      </ul>
+      </td>
+      <td>
+      <ul>
+        <li>YOLOv5PAFPN</li>
+        <li>YOLOv8PAFPN</li>
+        <li>YOLOv6RepPAFPN</li>
+        <li>YOLOXPAFPN</li>
+        <li>CSPNeXtPAFPN</li>
+        <li>YOLOv7PAFPN</li>
+        <li>PPYOLOECSPPAFPN</li>
+      </ul>
+      </td>
+      <td>
+        <ul>
+          <li>IoULoss</li>
+          <li>mmdet loss</li>
+        </ul>
+      </td>
+      <td>
+        <ul>
+        </ul>
+      </td>
+    </tr>
+</td>
+    </tr>
+  </tbody>
+</table>
+
+</details>
+
+## ❓ FAQ [🔝](#-table-of-contents)
+
+Please refer to the [FAQ](docs/en/tutorials/faq.md) for frequently asked questions.
+
+## 🙌 Contributing [🔝](#-table-of-contents)
+
+We appreciate all contributions to improving MMYOLO. Ongoing projects can be found in our [GitHub Projects](https://github.com/open-mmlab/mmyolo/projects). Welcome community users to participate in these projects. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
+
+## 🤝 Acknowledgement [🔝](#-table-of-contents)
+
+MMYOLO is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedback.
+We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new detectors.
+
+<div align="center">
+  <a href="https://github.com/open-mmlab/mmyolo/graphs/contributors"><img src="https://contrib.rocks/image?repo=open-mmlab/mmyolo"/></a>
+</div>
+
+## 🖊️ Citation [🔝](#-table-of-contents)
+
+If you find this project useful in your research, please consider citing:
+
+```latex
+@misc{mmyolo2022,
+    title={{MMYOLO: OpenMMLab YOLO} series toolbox and benchmark},
+    author={MMYOLO Contributors},
+    howpublished = {\url{https://github.com/open-mmlab/mmyolo}},
+    year={2022}
+}
+```
+
+## 🎫 License [🔝](#-table-of-contents)
+
+This project is released under the [GPL 3.0 license](LICENSE).
+
+## 🏗️ Projects in OpenMMLab [🔝](#-table-of-contents)
+
+- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
+- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.
+- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
+- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
+- [MMEval](https://github.com/open-mmlab/mmeval): OpenMMLab machine learning evaluation library.
+- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.

third_party/mmyolo/README_zh-CN.md

@@ -0,0 +1,468 @@
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/27466624/222385182-1247251c-8fac-4e77-94f5-57580e0ce3bd.png" width="100%"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">OpenMMLab 官网</font></b>
+    <sup>
+      <a href="https://openmmlab.com">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <b><font size="5">OpenMMLab 开放平台</font></b>
+    <sup>
+      <a href="https://platform.openmmlab.com">
+        <i><font size="4">TRY IT OUT</font></i>
+      </a>
+    </sup>
+  </div>
+  <div>&nbsp;</div>
+
+[![PyPI](https://img.shields.io/pypi/v/mmyolo)](https://pypi.org/project/mmyolo)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmyolo.readthedocs.io/zh_CN/latest/)
+[![deploy](https://github.com/open-mmlab/mmyolo/workflows/deploy/badge.svg)](https://github.com/open-mmlab/mmyolo/actions)
+[![codecov](https://codecov.io/gh/open-mmlab/mmyolo/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmyolo)
+[![license](https://img.shields.io/github/license/open-mmlab/mmyolo.svg)](https://github.com/open-mmlab/mmyolo/blob/main/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmyolo.svg)](https://github.com/open-mmlab/mmyolo/issues)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmyolo.svg)](https://github.com/open-mmlab/mmyolo/issues)
+
+[📘使用文档](https://mmyolo.readthedocs.io/zh_CN/latest/) |
+[🛠️安装教程](https://mmyolo.readthedocs.io/zh_CN/latest/get_started/installation.html) |
+[👀模型库](https://mmyolo.readthedocs.io/zh_CN/latest/model_zoo.html) |
+[🆕更新日志](https://mmyolo.readthedocs.io/zh_CN/latest/notes/changelog.html) |
+[🤔报告问题](https://github.com/open-mmlab/mmyolo/issues/new/choose)
+
+</div>
+
+<div align="center">
+
+[English](README.md) | 简体中文
+
+</div>
+
+<div align="center">
+  <a href="https://openmmlab.medium.com/" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://discord.com/channels/1037617289144569886/1046608014234370059" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
+</div>
+
+## 📄 Table of Contents
+
+- [🥳 🚀 最新进展](#--最新进展-)
+  - [✨ 亮点](#-亮点-)
+- [📖 简介](#-简介-)
+- [🛠️ 安装](#️%EF%B8%8F-安装-)
+- [👨‍🏫 教程](#-教程-)
+- [📊 基准测试和模型库](#-基准测试和模型库-)
+- [❓ 常见问题](#-常见问题-)
+- [🙌 贡献指南](#-贡献指南-)
+- [🤝 致谢](#🤝-致谢-)
+- [🖊️ 引用](#️-引用-)
+- [🎫 开源许可证](#-开源许可证-)
+- [🏗️ OpenMMLab 的其他项目](#%EF%B8%8F-openmmlab-的其他项目-)
+- [❤️ 欢迎加入 OpenMMLab 社区](#%EF%B8%8F-欢迎加入-openmmlab-社区-)
+
+## 🥳 🚀 最新进展 [🔝](#-table-of-contents)
+
+💎 **v0.6.0** 版本已经在 2023.8.15 发布：
+
+- 支持 YOLOv5 实例分割
+- 基于 MMPose 支持 YOLOX-Pose
+- 添加 15 分钟的实例分割教程
+- YOLOv5 支持使用 mask 标注来优化边界框
+- 添加多尺度训练和测试文档
+
+我们提供了实用的**脚本命令速查表**
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/27466624/213104312-3580c783-2423-442f-b5f6-79204a06adb5.png">
+</div>
+
+你可以点击[链接](https://pan.baidu.com/s/1QEaqT7YayUdEvh1an0gjHg?pwd=yolo)，下载高清版 PDF 文件。
+
+同时我们也推出了解读视频：
+
+|     |                内容                |                                                                                                                                                                                                     视频                                                                                                                                                                                                      |                                                                                                         课程中的代码                                                                                                          |
+| :-: | :--------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| 🌟  |            特征图可视化            | [![Link](https://i2.hdslb.com/bfs/archive/480a0eb41fce26e0acb65f82a74501418eee1032.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV188411s7o8)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV188411s7o8)](https://www.bilibili.com/video/BV188411s7o8) | [特征图可视化.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/%5B%E5%B7%A5%E5%85%B7%E7%B1%BB%E7%AC%AC%E4%B8%80%E6%9C%9F%5D%E7%89%B9%E5%BE%81%E5%9B%BE%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb) |
+| 🌟  |     源码阅读和调试「必备」技巧     | [![Link](https://i2.hdslb.com/bfs/archive/790d2422c879ff20488910da1c4422b667ea6af7.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1N14y1V7mB)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1N14y1V7mB)](https://www.bilibili.com/video/BV1N14y1V7mB) |                                                                           [源码阅读和调试「必备」技巧文档](https://zhuanlan.zhihu.com/p/580885852)                                                                            |
+| 🌟  |         10分钟换遍主干网络         | [![Link](http://i0.hdslb.com/bfs/archive/c51f1aef7c605856777249a7b4478f44bd69f3bd.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1JG4y1d7GC)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1JG4y1d7GC)](https://www.bilibili.com/video/BV1JG4y1d7GC)  |     [10分钟换遍主干网络文档](https://zhuanlan.zhihu.com/p/585641598)<br>[10分钟换遍主干网络.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[实用类第二期]10分钟换遍主干网络.ipynb)     |
+| 🌟  | 自定义数据集从标注到部署保姆级教程 | [![Link](https://i2.hdslb.com/bfs/archive/13f566c89a18c9c881713b63ec14da952d4c0b14.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1RG4y137i5)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1RG4y137i5)](https://www.bilibili.com/video/BV1JG4y1d7GC) |                                                 [自定义数据集从标注到部署保姆级教程](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/user_guides/custom_dataset.md)                                                  |
+| 🌟  |      顶会第一步 · 模块自定义       | [![Link](http://i2.hdslb.com/bfs/archive/5b23d41ac57466824eaf185ef806ef734414e93b.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1yd4y1j7VD)  [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1yd4y1j7VD)](https://www.bilibili.com/video/BV1yd4y1j7VD)  |                                    [顶会第一步·模块自定义.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[实用类第四期]顶会第一步·模块自定义.ipynb)                                    |
+
+完整视频列表请参考 [中文解��资源汇总 - 视频](https://mmyolo.readthedocs.io/zh_CN/latest/get_started/article.html)
+
+发布历史和更新细节请参考 [更新日志](https://mmyolo.readthedocs.io/zh_CN/latest/notes/changelog.html)
+
+### ✨ 亮点 [🔝](#-table-of-contents)
+
+我们很高兴向大家介绍我们在实时目标识别任务方面的最新成果 RTMDet，包含了一系列的全卷积单阶段检测模型。 RTMDet 不仅在从 tiny 到 extra-large 尺寸的目标检测模型上实现了最佳的参数量和精度的平衡，而且在实时实例分割和旋转目标检测任务上取得了最先进的成果。 更多细节请参阅[技术报告](https://arxiv.org/abs/2212.07784)。 预训练模型可以在[这里](configs/rtmdet)找到。
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/real-time-instance-segmentation-on-mscoco)](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-dota-1)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-hrsc2016)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
+
+| Task                     | Dataset | AP                                   | FPS(TRT FP16 BS1 3090) |
+| ------------------------ | ------- | ------------------------------------ | ---------------------- |
+| Object Detection         | COCO    | 52.8                                 | 322                    |
+| Instance Segmentation    | COCO    | 44.6                                 | 188                    |
+| Rotated Object Detection | DOTA    | 78.9(single-scale)/81.3(multi-scale) | 121                    |
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
+</div>
+
+MMYOLO 中目前实现了目标检测和旋转框目标检测算法，但是相比 MMDeteciton 版本有显著训练加速，训练速度相比原先版本提升 2.6 倍。
+
+## 📖 简介 [🔝](#-table-of-contents)
+
+MMYOLO 是一个基于 PyTorch 和 MMDetection 的 YOLO 系列算法开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
+
+主分支代码目前支持 PyTorch 1.6 以上的版本。
+<img src="https://user-images.githubusercontent.com/45811724/190993591-bd3f1f11-1c30-4b93-b5f4-05c9ff64ff7f.gif"/>
+
+<details open>
+<summary>主要特性</summary>
+
+- 🕹️ **统一便捷的算法评测**
+
+  MMYOLO 统一了各类 YOLO 算法模块的实现, 并提供了统一的评测流程，用户可以公平便捷地进行对比分析。
+
+- 📚 **丰富的入门和进阶文档**
+
+  MMYOLO 提供了从入门到部署到进阶和算法解析等一系列文档，方便不同用户快速上手和扩展。
+
+- 🧩 **模块化设计**
+
+  MMYOLO 将框架解耦成不同的模块组件，通过组合不同的模块和训练测试策略，用户可以便捷地构建自定义模型。
+
+<img src="https://user-images.githubusercontent.com/27466624/199999337-0544a4cb-3cbd-4f3e-be26-bcd9e74db7ff.jpg" alt="基类-P5"/>
+  图为 RangeKing@GitHub 提供，非常感谢！
+
+P6 模型图详见 [model_design.md](docs/zh_cn/recommended_topics/model_design.md)。
+
+</details>
+
+## 🛠️ 安装 [🔝](#-table-of-contents)
+
+MMYOLO 依赖 PyTorch, MMCV, MMEngine 和 MMDetection，以下是安装的简要步骤。 更详细的安装指南请参考[安装文档](docs/zh_cn/get_started/installation.md)。
+
+```shell
+conda create -n mmyolo python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
+conda activate mmyolo
+pip install openmim
+mim install "mmengine>=0.6.0"
+mim install "mmcv>=2.0.0rc4,<2.1.0"
+mim install "mmdet>=3.0.0,<4.0.0"
+git clone https://github.com/open-mmlab/mmyolo.git
+cd mmyolo
+# Install albumentations
+pip install -r requirements/albu.txt
+# Install MMYOLO
+mim install -v -e .
+```
+
+## 👨‍🏫 教程 [🔝](#-table-of-contents)
+
+MMYOLO 基于 MMDetection 开源库，并且采用相同的代码组织和设计方式。为了更好的使用本开源库，请先阅读 [MMDetection 概述](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html) 对 MMDetection 进行初步地了解。
+
+MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也可以了解 [MMDetection 用户指南和进阶指南](https://mmdetection.readthedocs.io/zh_CN/3.x/) 。
+
+针对和 MMDetection 不同的部分，我们也准备了用户指南和进阶指南，请阅读我们的 [文档](https://mmyolo.readthedocs.io/zh_CN/latest/) 。
+
+<details>
+<summary>开启 MMYOLO ���旅</summary>
+
+- [概述](docs/zh_cn/get_started/overview.md)
+- [依赖](docs/zh_cn/get_started/dependencies.md)
+- [安装和验证](docs/zh_cn/get_started/installation.md)
+- [15 分钟上手 MMYOLO 目标检测](docs/zh_cn/get_started/15_minutes_object_detection.md)
+- [15 分钟上手 MMYOLO 旋转框目标检测](docs/zh_cn/get_started/15_minutes_rotated_object_detection.md)
+- [15 分钟上手 MMYOLO 实例分割](docs/zh_cn/get_started/15_minutes_instance_segmentation.md)
+- [中文解读资源汇总](docs/zh_cn/get_started/article.md)
+
+</details>
+
+<details>
+<summary>推荐专题</summary>
+
+- [如何给 MMYOLO 贡献代码](docs/zh_cn/recommended_topics/contributing.md)
+- [训练和测试技巧](docs/zh_cn/recommended_topics/training_testing_tricks.md)
+- [MMYOLO 模型结构设计](docs/zh_cn/recommended_topics/model_design.md)
+- [原理和实现全解析](docs/zh_cn/recommended_topics/algorithm_descriptions/)
+- [轻松更换主干网络](docs/zh_cn/recommended_topics/replace_backbone.md)
+- [MMYOLO 模型复杂度分析](docs/zh_cn/recommended_topics/complexity_analysis.md)
+- [标注+训练+测试+部署全流程](docs/zh_cn/recommended_topics/labeling_to_deployment_tutorials.md)
+- [关于可视化的一切](docs/zh_cn/recommended_topics/visualization.md)
+- [模型部署流程](docs/zh_cn/recommended_topics/deploy/)
+- [常见错误排查步骤](docs/zh_cn/recommended_topics/troubleshooting_steps.md)
+- [MMYOLO 应用范例介绍](docs/zh_cn/recommended_topics/application_examples/)
+- [MM 系列 Repo 必备基础](docs/zh_cn/recommended_topics/mm_basics.md)
+- [数据集准备和说明](docs/zh_cn/recommended_topics/dataset_preparation.md)
+
+</details>
+
+<details>
+<summary>常用功能</summary>
+
+- [恢复训练](docs/zh_cn/common_usage/resume_training.md)
+- [开启和关闭 SyncBatchNorm](docs/zh_cn/common_usage/syncbn.md)
+- [开启混合精度训练](docs/zh_cn/common_usage/amp_training.md)
+- [多尺度训练和测试](docs/zh_cn/common_usage/ms_training_testing.md)
+- [测试时增强相关说明](docs/zh_cn/common_usage/tta.md)
+- [给主干网络增加插件](docs/zh_cn/common_usage/plugins.md)
+- [冻结指定网络层权重](docs/zh_cn/common_usage/freeze_layers.md)
+- [输出模型预测结果](docs/zh_cn/common_usage/output_predictions.md)
+- [设置随机种子](docs/zh_cn/common_usage/set_random_seed.md)
+- [算法组合替换教程](docs/zh_cn/common_usage/module_combination.md)
+- [使用 mim 跨库调用其他 OpenMMLab 仓库的脚本](docs/zh_cn/common_usage/mim_usage.md)
+- [应用多个 Neck](docs/zh_cn/common_usage/multi_necks.md)
+- [指定特定设备训练或推理](docs/zh_cn/common_usage/specify_device.md)
+- [单通道和多通道应用案例](docs/zh_cn/common_usage/single_multi_channel_applications.md)
+- [MM 系列开源库注册表](docs/zh_cn/common_usage/registries_info.md)
+
+</details>
+
+<details>
+<summary>实用工具</summary>
+
+- [可视化 COCO 标签](docs/zh_cn/useful_tools/browse_coco_json.md)
+- [可视化数据集](docs/zh_cn/useful_tools/browse_dataset.md)
+- [打印完整配置文件](docs/zh_cn/useful_tools/print_config.md)
+- [可视化数据集分析结果](docs/zh_cn/useful_tools/dataset_analysis.md)
+- [优化锚框尺寸](docs/zh_cn/useful_tools/optimize_anchors.md)
+- [提取 COCO 子集](docs/zh_cn/useful_tools/extract_subcoco.md)
+- [可视化优化器参数策略](docs/zh_cn/useful_tools/vis_scheduler.md)
+- [数据集转换](docs/zh_cn/useful_tools/dataset_converters.md)
+- [数据集下载](docs/zh_cn/useful_tools/download_dataset.md)
+- [日志分析](docs/zh_cn/useful_tools/log_analysis.md)
+- [模型转换](docs/zh_cn/useful_tools/model_converters.md)
+
+</details>
+
+<details>
+<summary>基础教程</summary>
+
+- [学习 YOLOv5 配置文件](docs/zh_cn/tutorials/config.md)
+- [数据流](docs/zh_cn/tutorials/data_flow.md)
+- [旋转目标检测](docs/zh_cn/tutorials/rotated_detection.md)
+- [自定义安装](docs/zh_cn/tutorials/custom_installation.md)
+- [常见警告说明](docs/zh_cn/tutorials/warning_notes.md)
+- [常见问题](docs/zh_cn/tutorials/faq.md)
+
+</details>
+
+<details>
+<summary>进阶教程</summary>
+
+- [MMYOLO 跨库应用解析](docs/zh_cn/advanced_guides/cross-library_application.md)
+
+</details>
+
+<details>
+<summary>说明</summary>
+
+- [更新日志](docs/zh_cn/notes/changelog.md)
+- [兼容性说明](docs/zh_cn/notes/compatibility.md)
+- [默认约定](docs/zh_cn/notes/conventions.md)
+- [代码规范](docs/zh_cn/notes/code_style.md)
+
+</details>
+
+## 📊 基准测试和模型库 [🔝](#-table-of-contents)
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/17425982/222087414-168175cc-dae6-4c5c-a8e3-3109a152dd19.png"/>
+</div>
+
+测试结果和模型可以在 [模型库](docs/zh_cn/model_zoo.md) 中找到。
+
+<details open>
+<summary><b>支持的任务</b></summary>
+
+- [x] 目标检测
+- [x] 旋转框目标检测
+
+</details>
+
+<details open>
+<summary><b>支持的算法</b></summary>
+
+- [x] [YOLOv5](configs/yolov5)
+- [ ] [YOLOv5u](configs/yolov5/yolov5u) (��推理)
+- [x] [YOLOX](configs/yolox)
+- [x] [RTMDet](configs/rtmdet)
+- [x] [RTMDet-Rotated](configs/rtmdet)
+- [x] [YOLOv6](configs/yolov6)
+- [x] [YOLOv7](configs/yolov7)
+- [x] [PPYOLOE](configs/ppyoloe)
+- [x] [YOLOv8](configs/yolov8)
+
+</details>
+
+<details open>
+<summary><b>支持的数据集</b></summary>
+
+- [x] COCO Dataset
+- [x] VOC Dataset
+- [x] CrowdHuman Dataset
+- [x] DOTA 1.0 Dataset
+
+</details>
+
+<details open>
+<div align="center">
+  <b>模块组件</b>
+</div>
+<table align="center">
+  <tbody>
+    <tr align="center" valign="bottom">
+      <td>
+        <b>Backbones</b>
+      </td>
+      <td>
+        <b>Necks</b>
+      </td>
+      <td>
+        <b>Loss</b>
+      </td>
+      <td>
+        <b>Common</b>
+      </td>
+    </tr>
+    <tr valign="top">
+      <td>
+      <ul>
+        <li>YOLOv5CSPDarknet</li>
+        <li>YOLOv8CSPDarknet</li>
+        <li>YOLOXCSPDarknet</li>
+        <li>EfficientRep</li>
+        <li>CSPNeXt</li>
+        <li>YOLOv7Backbone</li>
+        <li>PPYOLOECSPResNet</li>
+        <li>mmdet backbone</li>
+        <li>mmcls backbone</li>
+        <li>timm</li>
+      </ul>
+      </td>
+      <td>
+      <ul>
+        <li>YOLOv5PAFPN</li>
+        <li>YOLOv8PAFPN</li>
+        <li>YOLOv6RepPAFPN</li>
+        <li>YOLOXPAFPN</li>
+        <li>CSPNeXtPAFPN</li>
+        <li>YOLOv7PAFPN</li>
+        <li>PPYOLOECSPPAFPN</li>
+      </ul>
+      </td>
+      <td>
+        <ul>
+          <li>IoULoss</li>
+          <li>mmdet loss</li>
+        </ul>
+      </td>
+      <td>
+        <ul>
+        </ul>
+      </td>
+    </tr>
+</td>
+    </tr>
+  </tbody>
+</table>
+
+</details>
+
+## ❓ 常见问题 [🔝](#-table-of-contents)
+
+请参考 [FAQ](docs/zh_cn/tutorials/faq.md) 了解其他用户的常见问题。
+
+## 🙌 贡献指南 [🔝](#-table-of-contents)
+
+我们感谢所有的贡献者为改进和提升 MMYOLO 所作出的努力。我们将正在进行中的项目添加进了[GitHub Projects](https://github.com/open-mmlab/mmyolo/projects)页面，非常欢迎社区用户能参与进这些项目中来。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
+
+## 🤝 致谢 [🔝](#-table-of-contents)
+
+MMYOLO 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。
+
+<div align="center">
+  <a href="https://github.com/open-mmlab/mmyolo/graphs/contributors"><img src="https://contrib.rocks/image?repo=open-mmlab/mmyolo"/></a>
+</div>
+
+## 🖊️ 引用 [🔝](#-table-of-contents)
+
+如果你觉得本项目对你的研究工作有所帮助，请参考如下 bibtex 引用 MMYOLO
+
+```latex
+@misc{mmyolo2022,
+    title={{MMYOLO: OpenMMLab YOLO} series toolbox and benchmark},
+    author={MMYOLO Contributors},
+    howpublished = {\url{https://github.com/open-mmlab/mmyolo}},
+    year={2022}
+}
+```
+
+## 🎫 开源许可证 [🔝](#-table-of-contents)
+
+该项目采用 [GPL 3.0 开源许可证](LICENSE)。
+
+## 🏗️ OpenMMLab 的其他项目 [🔝](#-table-of-contents)
+
+- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
+- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab 深度学习预训练工具箱
+- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab 新一代人工智能内容生成（AIGC）工具箱
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
+- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
+- [MMEval](https://github.com/open-mmlab/mmeval): OpenMMLab 机器学习算法评测库
+- [Playground](https://github.com/open-mmlab/playground): 收集和展示 OpenMMLab 相关的前沿、有趣的社区项目
+
+## ❤️ 欢迎加入 OpenMMLab 社区 [🔝](#-table-of-contents)
+
+扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3)
+
+<div align="center">
+<img src="resources/zhihu_qrcode.jpg" height="400" />  <img src="resources/qq_group_qrcode.jpg" height="400" />
+</div>
+
+我们会在 OpenMMLab 社区为大家
+
+- 📢 分享 AI 框架的前沿核心技术
+- 💻 解读 PyTorch 常用模块源码
+- 📰 发布 OpenMMLab 的相关新闻
+- 🚀 介绍 OpenMMLab 开发的前沿算法
+- 🏃 获取更高效的问题答疑和意见反馈
+- 🔥 提供与各行各业开发者充分交流的平台
+
+干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬

third_party/mmyolo/configs/_base_/default_runtime.py

@@ -0,0 +1,43 @@
+default_scope = 'mmyolo'
+
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=1),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='mmdet.DetVisualizationHook'))
+
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='mmdet.DetLocalVisualizer',
+    vis_backends=vis_backends,
+    name='visualizer')
+log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
+
+log_level = 'INFO'
+load_from = None
+resume = False
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions
+# before MMDet 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+
+backend_args = None

third_party/mmyolo/configs/_base_/det_p5_tta.py

@@ -0,0 +1,58 @@
+# TODO: Need to solve the problem of multiple backend_args parameters
+# _backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+
+_backend_args = None
+
+tta_model = dict(
+    type='mmdet.DetTTAModel',
+    tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.65), max_per_img=300))
+
+img_scales = [(640, 640), (320, 320), (960, 960)]
+
+#                                LoadImageFromFile
+#                     /                 |                     \
+# (RatioResize,LetterResize) (RatioResize,LetterResize) (RatioResize,LetterResize) # noqa
+#        /      \                    /      \                    /        \
+#  RandomFlip RandomFlip      RandomFlip RandomFlip        RandomFlip RandomFlip # noqa
+#      |          |                |         |                  |         |
+#  LoadAnn    LoadAnn           LoadAnn    LoadAnn           LoadAnn    LoadAnn
+#      |          |                |         |                  |         |
+#  PackDetIn  PackDetIn         PackDetIn  PackDetIn        PackDetIn  PackDetIn # noqa
+
+_multiscale_resize_transforms = [
+    dict(
+        type='Compose',
+        transforms=[
+            dict(type='YOLOv5KeepRatioResize', scale=s),
+            dict(
+                type='LetterResize',
+                scale=s,
+                allow_scale_up=False,
+                pad_val=dict(img=114))
+        ]) for s in img_scales
+]
+
+tta_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=_backend_args),
+    dict(
+        type='TestTimeAug',
+        transforms=[
+            _multiscale_resize_transforms,
+            [
+                dict(type='mmdet.RandomFlip', prob=1.),
+                dict(type='mmdet.RandomFlip', prob=0.)
+            ], [dict(type='mmdet.LoadAnnotations', with_bbox=True)],
+            [
+                dict(
+                    type='mmdet.PackDetInputs',
+                    meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                               'scale_factor', 'pad_param', 'flip',
+                               'flip_direction'))
+            ]
+        ])
+]

third_party/mmyolo/configs/_base_/pose/coco.py

@@ -0,0 +1,181 @@
+dataset_info = dict(
+    dataset_name='coco',
+    paper_info=dict(
+        author='Lin, Tsung-Yi and Maire, Michael and '
+        'Belongie, Serge and Hays, James and '
+        'Perona, Pietro and Ramanan, Deva and '
+        r'Doll{\'a}r, Piotr and Zitnick, C Lawrence',
+        title='Microsoft coco: Common objects in context',
+        container='European conference on computer vision',
+        year='2014',
+        homepage='http://cocodataset.org/',
+    ),
+    keypoint_info={
+        0:
+        dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
+        1:
+        dict(
+            name='left_eye',
+            id=1,
+            color=[51, 153, 255],
+            type='upper',
+            swap='right_eye'),
+        2:
+        dict(
+            name='right_eye',
+            id=2,
+            color=[51, 153, 255],
+            type='upper',
+            swap='left_eye'),
+        3:
+        dict(
+            name='left_ear',
+            id=3,
+            color=[51, 153, 255],
+            type='upper',
+            swap='right_ear'),
+        4:
+        dict(
+            name='right_ear',
+            id=4,
+            color=[51, 153, 255],
+            type='upper',
+            swap='left_ear'),
+        5:
+        dict(
+            name='left_shoulder',
+            id=5,
+            color=[0, 255, 0],
+            type='upper',
+            swap='right_shoulder'),
+        6:
+        dict(
+            name='right_shoulder',
+            id=6,
+            color=[255, 128, 0],
+            type='upper',
+            swap='left_shoulder'),
+        7:
+        dict(
+            name='left_elbow',
+            id=7,
+            color=[0, 255, 0],
+            type='upper',
+            swap='right_elbow'),
+        8:
+        dict(
+            name='right_elbow',
+            id=8,
+            color=[255, 128, 0],
+            type='upper',
+            swap='left_elbow'),
+        9:
+        dict(
+            name='left_wrist',
+            id=9,
+            color=[0, 255, 0],
+            type='upper',
+            swap='right_wrist'),
+        10:
+        dict(
+            name='right_wrist',
+            id=10,
+            color=[255, 128, 0],
+            type='upper',
+            swap='left_wrist'),
+        11:
+        dict(
+            name='left_hip',
+            id=11,
+            color=[0, 255, 0],
+            type='lower',
+            swap='right_hip'),
+        12:
+        dict(
+            name='right_hip',
+            id=12,
+            color=[255, 128, 0],
+            type='lower',
+            swap='left_hip'),
+        13:
+        dict(
+            name='left_knee',
+            id=13,
+            color=[0, 255, 0],
+            type='lower',
+            swap='right_knee'),
+        14:
+        dict(
+            name='right_knee',
+            id=14,
+            color=[255, 128, 0],
+            type='lower',
+            swap='left_knee'),
+        15:
+        dict(
+            name='left_ankle',
+            id=15,
+            color=[0, 255, 0],
+            type='lower',
+            swap='right_ankle'),
+        16:
+        dict(
+            name='right_ankle',
+            id=16,
+            color=[255, 128, 0],
+            type='lower',
+            swap='left_ankle')
+    },
+    skeleton_info={
+        0:
+        dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
+        1:
+        dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
+        2:
+        dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
+        3:
+        dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
+        4:
+        dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
+        5:
+        dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
+        6:
+        dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
+        7:
+        dict(
+            link=('left_shoulder', 'right_shoulder'),
+            id=7,
+            color=[51, 153, 255]),
+        8:
+        dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
+        9:
+        dict(
+            link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
+        10:
+        dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
+        11:
+        dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
+        12:
+        dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
+        13:
+        dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
+        14:
+        dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
+        15:
+        dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
+        16:
+        dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
+        17:
+        dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
+        18:
+        dict(
+            link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])
+    },
+    joint_weights=[
+        1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
+        1.5
+    ],
+    sigmas=[
+        0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
+        0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
+    ])

third_party/mmyolo/configs/deploy/base_dynamic.py

@@ -0,0 +1,17 @@
+_base_ = ['./base_static.py']
+onnx_config = dict(
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+        'dets': {
+            0: 'batch',
+            1: 'num_dets'
+        },
+        'labels': {
+            0: 'batch',
+            1: 'num_dets'
+        }
+    })

third_party/mmyolo/configs/deploy/base_static.py

@@ -0,0 +1,23 @@
+onnx_config = dict(
+    type='onnx',
+    export_params=True,
+    keep_initializers_as_inputs=False,
+    opset_version=11,
+    save_file='end2end.onnx',
+    input_names=['input'],
+    output_names=['dets', 'labels'],
+    input_shape=None,
+    optimize=True)
+codebase_config = dict(
+    type='mmyolo',
+    task='ObjectDetection',
+    model_type='end2end',
+    post_processing=dict(
+        score_threshold=0.05,
+        confidence_threshold=0.005,
+        iou_threshold=0.5,
+        max_output_boxes_per_class=200,
+        pre_top_k=5000,
+        keep_top_k=100,
+        background_label_id=-1),
+    module=['mmyolo.deploy'])

third_party/mmyolo/configs/deploy/detection_onnxruntime_dynamic.py

@@ -0,0 +1,15 @@
+_base_ = ['./base_dynamic.py']
+codebase_config = dict(
+    type='mmyolo',
+    task='ObjectDetection',
+    model_type='end2end',
+    post_processing=dict(
+        score_threshold=0.05,
+        confidence_threshold=0.005,
+        iou_threshold=0.5,
+        max_output_boxes_per_class=200,
+        pre_top_k=5000,
+        keep_top_k=100,
+        background_label_id=-1),
+    module=['mmyolo.deploy'])
+backend_config = dict(type='onnxruntime')