Spaces:
Running
on
T4
Running
on
T4
| _base_ = 'yolov5_s-v61_fast_1xb12-40e_cat.py' | |
| # This configuration is used to provide non-square training examples | |
| # Must be a multiple of 32 | |
| img_scale = (608, 352) # w h | |
| anchors = [ | |
| [(65, 35), (159, 45), (119, 80)], # P3/8 | |
| [(215, 77), (224, 116), (170, 166)], # P4/16 | |
| [(376, 108), (339, 176), (483, 190)] # P5/32 | |
| ] | |
| # ===============================Unmodified in most cases==================== | |
| _base_.model.bbox_head.loss_obj.loss_weight = 1.0 * ((img_scale[1] / 640)**2) | |
| _base_.model.bbox_head.prior_generator.base_sizes = anchors | |
| train_pipeline = [ | |
| *_base_.pre_transform, | |
| dict( | |
| type='Mosaic', | |
| img_scale=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), | |
| # img_scale is (width, height) | |
| border=(-img_scale[0] // 2, -img_scale[1] // 2), | |
| border_val=(114, 114, 114)), | |
| 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='mmdet.PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', | |
| 'flip_direction')) | |
| ] | |
| _base_.train_dataloader.dataset.pipeline = train_pipeline | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | |
| dict(type='YOLOv5KeepRatioResize', scale=img_scale), | |
| dict( | |
| type='LetterResize', | |
| scale=img_scale, | |
| allow_scale_up=False, | |
| pad_val=dict(img=114)), | |
| 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')) | |
| ] | |
| val_dataloader = dict( | |
| dataset=dict(pipeline=test_pipeline, batch_shapes_cfg=None)) | |
| test_dataloader = val_dataloader | |