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config.py

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+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint=
+            'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth',
+            prefix='backbone')),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=2,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, )))
+dataset_type = 'CustomDataset'
+classes = ['No', 'Yes']
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', size=224),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=4,
+    train=dict(
+        type='CustomDataset',
+        data_prefix='/work/home/acy25a367n/pornpics/pornpics-download-s',
+        ann_file=
+        '/work/home/acy25a367n/mmclassification/pornpics/outdoor/outdoor_train.csv',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='RandomResizedCrop', size=224),
+            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='ToTensor', keys=['gt_label']),
+            dict(type='Collect', keys=['img', 'gt_label'])
+        ]),
+    val=dict(
+        type='CustomDataset',
+        data_prefix='/work/home/acy25a367n/pornpics/pornpics-download-s',
+        ann_file=
+        '/work/home/acy25a367n/mmclassification/pornpics/outdoor/outdoor_valid.csv',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='Resize', size=(256, -1)),
+            dict(type='CenterCrop', crop_size=224),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ]),
+    test=dict(
+        type='CustomDataset',
+        data_prefix='/work/home/acy25a367n/pornpics/pornpics-download-s',
+        ann_file=
+        '/work/home/acy25a367n/mmclassification/pornpics/outdoor/outdoor_valid.csv',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='Resize', size=(256, -1)),
+            dict(type='CenterCrop', crop_size=224),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ]))
+evaluation = dict(
+    interval=1, metric='accuracy', metric_options=dict(topk=(1, )))
+optimizer = dict(
+    type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005, nesterov=True)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_iters=5,
+    warmup_ratio=0.01,
+    warmup_by_epoch=True)
+runner = dict(type='EpochBasedRunner', max_epochs=100)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=4, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth'
+work_dir = 'work_dirs/resnet50_8xb32_outdoor'
+gpu_ids = [0]

model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7cbe61cb733167e29c8c3e4e52fd0de5babcb10e6a34d9f02001a9cf3d3a2292
+size 188469887