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vasgaowei / TS-CAM

Codes for TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization.
https://openaccess.thecvf.com/content/ICCV2021/papers/Gao_TS-CAM_Token_Semantic_Coupled_Attention_Map_for_Weakly_Supervised_Object_ICCV_2021_paper.pdf
Apache License 2.0
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Question about the training process #14

Open Unrealluver opened 2 years ago

Unrealluver commented 2 years ago

Greetings! Thanks for your excellent work! When running your code, I met a problem that the performance is poor. My running command is 

 bash train_val_cub.sh 3 deit small 224

and I got the log like:

{'BASIC': {'BACKUP_CODES': True,
           'BACKUP_LIST': ['lib', 'tools_cam', 'configs'],
           'DISP_FREQ': 10,
           'GPU_ID': [0],
           'NUM_WORKERS': 40,
           'ROOT_DIR': './tools_cam/..',
           'SAVE_DIR': 'ckpt/CUB/deit_tscam_small_patch16_224_CAM-NORMAL_SEED26_CAM-THR0.1_BS128_2022-03-25-01-46',
           'SEED': 26,
           'TIME': '2022-03-25-01-46'},
 'CUDNN': {'BENCHMARK': False, 'DETERMINISTIC': True, 'ENABLE': True},
 'DATA': {'CROP_SIZE': 224,
          'DATADIR': 'data/CUB_200_2011',
          'DATASET': 'CUB',
          'IMAGE_MEAN': [0.485, 0.456, 0.406],
          'IMAGE_STD': [0.229, 0.224, 0.225],
          'NUM_CLASSES': 200,
          'RESIZE_SIZE': 256,
          'SCALE_LENGTH': 15,
          'SCALE_SIZE': 196,
          'TRAIN_AUG_PATH': '',
          'VAL_PATH': ''},
 'MODEL': {'ARCH': 'deit_tscam_small_patch16_224',
           'CAM_THR': 0.1,
           'LOCALIZER_DIR': '',
           'TOP_K': 1},
 'SOLVER': {'LR_FACTOR': 0.1,
            'LR_STEPS': [30],
            'MUMENTUM': 0.9,
            'NUM_EPOCHS': 60,
            'START_LR': 0.001,
            'WEIGHT_DECAY': 0.0005},
 'TEST': {'BATCH_SIZE': 128,
          'CKPT_DIR': '',
          'SAVE_BOXED_IMAGE': False,
          'SAVE_CAMS': False,
          'TEN_CROPS': False},
 'TRAIN': {'ALPHA': 1.0,
           'BATCH_SIZE': 128,
           'BETA': 1.0,
           'IF_FIX_WEIGHT': False}}
==> Preparing data...
done!
==> Preparing networks for baseline...
Removing key head.weight from pretrained checkpoint
Removing key head.bias from pretrained checkpoint
TSCAM(
  (patch_embed): PatchEmbed(
    (proj): Conv2d(3, 384, kernel_size=(16, 16), stride=(16, 16))
  )
  (pos_drop): Dropout(p=0.0, inplace=False)
  (blocks): ModuleList(
    (0): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): Identity()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (1): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (2): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (3): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (4): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (5): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (6): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (7): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (8): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (9): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (10): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (11): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
  )
  (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
  (head): Conv2d(384, 200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (avgpool): AdaptiveAvgPool2d(output_size=1)
){'BASIC': {'BACKUP_CODES': True,
           'BACKUP_LIST': ['lib', 'tools_cam', 'configs'],
           'DISP_FREQ': 10,
           'GPU_ID': [0],
           'NUM_WORKERS': 40,
           'ROOT_DIR': './tools_cam/..',
           'SAVE_DIR': 'ckpt/CUB/deit_tscam_small_patch16_224_CAM-NORMAL_SEED26_CAM-THR0.1_BS128_2022-03-25-01-46',
           'SEED': 26,
           'TIME': '2022-03-25-01-46'},
 'CUDNN': {'BENCHMARK': False, 'DETERMINISTIC': True, 'ENABLE': True},
 'DATA': {'CROP_SIZE': 224,
          'DATADIR': 'data/CUB_200_2011',
          'DATASET': 'CUB',
          'IMAGE_MEAN': [0.485, 0.456, 0.406],
          'IMAGE_STD': [0.229, 0.224, 0.225],
          'NUM_CLASSES': 200,
          'RESIZE_SIZE': 256,
          'SCALE_LENGTH': 15,
          'SCALE_SIZE': 196,
          'TRAIN_AUG_PATH': '',
          'VAL_PATH': ''},
 'MODEL': {'ARCH': 'deit_tscam_small_patch16_224',
           'CAM_THR': 0.1,
           'LOCALIZER_DIR': '',
           'TOP_K': 1},
 'SOLVER': {'LR_FACTOR': 0.1,
            'LR_STEPS': [30],
            'MUMENTUM': 0.9,
            'NUM_EPOCHS': 60,
            'START_LR': 0.001,
            'WEIGHT_DECAY': 0.0005},
 'TEST': {'BATCH_SIZE': 128,
          'CKPT_DIR': '',
          'SAVE_BOXED_IMAGE': False,
          'SAVE_CAMS': False,
          'TEN_CROPS': False},
 'TRAIN': {'ALPHA': 1.0, 'BATCH_SIZE': 128, 'BETA': 1.0, 'IF_FIX_WEIGHT': True}}
==> Preparing data...
done!
==> Preparing networks for baseline...
Removing key head.weight from pretrained checkpoint
Removing key head.bias from pretrained checkpoint
TSCAM(
  (patch_embed): PatchEmbed(
    (proj): Conv2d(3, 384, kernel_size=(16, 16), stride=(16, 16))
  )
  (pos_drop): Dropout(p=0.0, inplace=False)
  (blocks): ModuleList(
    (0): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): Identity()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (1): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (2): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (3): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (4): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (5): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (6): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (7): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (8): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (9): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (10): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (11): Block(
      (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=384, out_features=1152, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=384, out_features=384, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=384, out_features=1536, bias=True)
        (act): GELU()
        (fc2): Linear(in_features=1536, out_features=384, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
  )
  (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
  (head): Conv2d(384, 200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (avgpool): AdaptiveAvgPool2d(output_size=1)
)
Preparing networks done!
Train Epoch: [1][1/47],lr: 0.00005  Loss 5.3910 (5.3910)    Prec@1 0.781 (0.781)    Prec@5 3.125 (3.125)
Train Epoch: [1][11/47],lr: 0.00005 Loss 5.2794 (5.3558)    Prec@1 1.562 (0.781)    Prec@5 7.031 (2.983)
Train Epoch: [1][21/47],lr: 0.00005 Loss 5.2877 (5.3156)    Prec@1 0.781 (0.818)    Prec@5 4.688 (3.162)
Train Epoch: [1][31/47],lr: 0.00005 Loss 5.1760 (5.2851)    Prec@1 0.781 (0.882)    Prec@5 7.031 (3.931)
Train Epoch: [1][41/47],lr: 0.00005 Loss 5.1290 (5.2508)    Prec@1 6.250 (1.524)    Prec@5 12.500 (5.812)
Train Epoch: [1][47/47],lr: 0.00005 Loss 5.1377 (5.2344)    Prec@1 3.774 (1.668)    Prec@5 11.321 (6.540)
Val Epoch: [1][1/46]    Loss 4.9150 (4.9150)    
Cls@1:0.125 Cls@5:0.320
Loc@1:0.031 Loc@5:0.055 Loc_gt:0.258

Val Epoch: [1][11/46]   Loss 4.7868 (5.0117)    
Cls@1:0.065 Cls@5:0.232
Loc@1:0.011 Loc@5:0.044 Loc_gt:0.214

Val Epoch: [1][21/46]   Loss 5.0634 (5.0060)    
Cls@1:0.066 Cls@5:0.232
Loc@1:0.015 Loc@5:0.052 Loc_gt:0.217

Val Epoch: [1][31/46]   Loss 5.1113 (5.0342)    
Cls@1:0.061 Cls@5:0.206
Loc@1:0.014 Loc@5:0.046 Loc_gt:0.198

Val Epoch: [1][41/46]   Loss 5.0010 (5.0245)    
Cls@1:0.059 Cls@5:0.204
Loc@1:0.014 Loc@5:0.046 Loc_gt:0.192

Val Epoch: [1][46/46]   Loss 4.8866 (5.0296)    
Cls@1:0.059 Cls@5:0.200
Loc@1:0.013 Loc@5:0.045 Loc_gt:0.189

wrong_details:75 5454 0 6 254 5
Best GT_LOC: 0.18916120124266483
Best TOP1_LOC: 0.18916120124266483
2022-03-25-01-49
Train Epoch: [2][1/47],lr: 0.00005  Loss 5.0064 (5.0064)    Prec@1 6.250 (6.250)    Prec@5 17.969 (17.969)
Train Epoch: [2][11/47],lr: 0.00005 Loss 4.9585 (4.9966)    Prec@1 6.250 (6.818)    Prec@5 21.875 (22.656)
Train Epoch: [2][21/47],lr: 0.00005 Loss 4.9573 (4.9768)    Prec@1 8.594 (6.734)    Prec@5 28.906 (24.479)
Train Epoch: [2][31/47],lr: 0.00005 Loss 4.9050 (4.9509)    Prec@1 10.938 (7.737)   Prec@5 28.125 (25.932)
Train Epoch: [2][41/47],lr: 0.00005 Loss 4.8085 (4.9271)    Prec@1 14.844 (8.841)   Prec@5 37.500 (27.458)
Train Epoch: [2][47/47],lr: 0.00005 Loss 4.8456 (4.9160)    Prec@1 8.491 (9.059)    Prec@5 31.132 (28.195)
Val Epoch: [2][1/46]    Loss 4.5358 (4.5358)    
Cls@1:0.258 Cls@5:0.523
Loc@1:0.078 Loc@5:0.164 Loc_gt:0.344

Val Epoch: [2][11/46]   Loss 4.3821 (4.7243)    
Cls@1:0.164 Cls@5:0.431
Loc@1:0.045 Loc@5:0.109 Loc_gt:0.240

Val Epoch: [2][21/46]   Loss 4.8342 (4.6906)    
Cls@1:0.173 Cls@5:0.453
Loc@1:0.059 Loc@5:0.135 Loc_gt:0.251

Val Epoch: [2][31/46]   Loss 4.9996 (4.7545)    
Cls@1:0.153 Cls@5:0.403
Loc@1:0.050 Loc@5:0.115 Loc_gt:0.225

Val Epoch: [2][41/46]   Loss 4.8124 (4.7559)    
Cls@1:0.138 Cls@5:0.385
Loc@1:0.045 Loc@5:0.108 Loc_gt:0.217

Val Epoch: [2][46/46]   Loss 4.8159 (4.7612)    
Cls@1:0.142 Cls@5:0.391
Loc@1:0.045 Loc@5:0.108 Loc_gt:0.213

wrong_details:263 4971 0 21 536 3
Best GT_LOC: 0.2126337590610977
Best TOP1_LOC: 0.2126337590610977
2022-03-25-01-54
Train Epoch: [3][1/47],lr: 0.00005  Loss 4.7283 (4.7283)    Prec@1 21.094 (21.094)  Prec@5 46.875 (46.875)
Train Epoch: [3][11/47],lr: 0.00005 Loss 4.7234 (4.7402)    Prec@1 11.719 (15.483)  Prec@5 45.312 (43.111)
Train Epoch: [3][21/47],lr: 0.00005 Loss 4.6686 (4.7088)    Prec@1 15.625 (16.332)  Prec@5 45.312 (43.824)
Train Epoch: [3][31/47],lr: 0.00005 Loss 4.6701 (4.6906)    Prec@1 20.312 (16.608)  Prec@5 46.875 (43.800)
Train Epoch: [3][41/47],lr: 0.00005 Loss 4.5544 (4.6702)    Prec@1 26.562 (17.073)  Prec@5 50.000 (44.284)
Train Epoch: [3][47/47],lr: 0.00005 Loss 4.5622 (4.6585)    Prec@1 26.415 (17.718)  Prec@5 49.057 (44.745)
Val Epoch: [3][1/46]    Loss 4.1796 (4.1796)    
Cls@1:0.336 Cls@5:0.711
Loc@1:0.156 Loc@5:0.312 Loc_gt:0.438

Val Epoch: [3][11/46]   Loss 4.0685 (4.4652)    
Cls@1:0.263 Cls@5:0.551
Loc@1:0.078 Loc@5:0.164 Loc_gt:0.273

Val Epoch: [3][21/46]   Loss 4.6838 (4.4194)    
Cls@1:0.264 Cls@5:0.570
Loc@1:0.098 Loc@5:0.198 Loc_gt:0.294

Val Epoch: [3][31/46]   Loss 4.8199 (4.5032)    
Cls@1:0.232 Cls@5:0.515
Loc@1:0.083 Loc@5:0.167 Loc_gt:0.260

Val Epoch: [3][41/46]   Loss 4.6710 (4.5206)    
Cls@1:0.209 Cls@5:0.494
Loc@1:0.073 Loc@5:0.156 Loc_gt:0.250

Val Epoch: [3][46/46]   Loss 4.4396 (4.5273)    
Cls@1:0.213 Cls@5:0.501
Loc@1:0.072 Loc@5:0.153 Loc_gt:0.243

wrong_details:420 4557 0 45 757 15
Best GT_LOC: 0.24318260269244046
Best TOP1_LOC: 0.24318260269244046
2022-03-25-01-59
Train Epoch: [4][1/47],lr: 0.00005  Loss 4.4849 (4.4849)    Prec@1 21.875 (21.875)  Prec@5 58.594 (58.594)
Train Epoch: [4][11/47],lr: 0.00005 Loss 4.5143 (4.4929)    Prec@1 28.125 (25.284)  Prec@5 47.656 (55.185)
Train Epoch: [4][21/47],lr: 0.00005 Loss 4.3787 (4.4674)    Prec@1 22.656 (25.744)  Prec@5 56.250 (55.357)
Train Epoch: [4][31/47],lr: 0.00005 Loss 4.3940 (4.4535)    Prec@1 31.250 (25.731)  Prec@5 52.344 (54.940)
Train Epoch: [4][41/47],lr: 0.00005 Loss 4.3730 (4.4333)    Prec@1 21.875 (26.067)  Prec@5 59.375 (55.259)
Train Epoch: [4][47/47],lr: 0.00005 Loss 4.3386 (4.4240)    Prec@1 28.302 (26.376)  Prec@5 64.151 (55.672)
Val Epoch: [4][1/46]    Loss 3.8875 (3.8875)    
Cls@1:0.383 Cls@5:0.758
Loc@1:0.203 Loc@5:0.398 Loc_gt:0.484

Val Epoch: [4][11/46]   Loss 3.8129 (4.2537)    
Cls@1:0.312 Cls@5:0.580
Loc@1:0.114 Loc@5:0.204 Loc_gt:0.298

Val Epoch: [4][21/46]   Loss 4.5173 (4.1790)    
Cls@1:0.326 Cls@5:0.619
Loc@1:0.137 Loc@5:0.244 Loc_gt:0.330

Val Epoch: [4][31/46]   Loss 4.6776 (4.2892)    
Cls@1:0.285 Cls@5:0.564
Loc@1:0.115 Loc@5:0.205 Loc_gt:0.290

Val Epoch: [4][41/46]   Loss 4.4627 (4.3164)    
Cls@1:0.263 Cls@5:0.547
Loc@1:0.102 Loc@5:0.190 Loc_gt:0.277

Val Epoch: [4][46/46]   Loss 4.2653 (4.3204)    
Cls@1:0.270 Cls@5:0.557
Loc@1:0.100 Loc@5:0.186 Loc_gt:0.269

wrong_details:580 4232 0 75 889 18
Best GT_LOC: 0.26855367621677595
Best TOP1_LOC: 0.26855367621677595
2022-03-25-02-01
Train Epoch: [5][1/47],lr: 0.00005  Loss 4.3349 (4.3349)    Prec@1 32.812 (32.812)  Prec@5 57.031 (57.031)
Train Epoch: [5][11/47],lr: 0.00005 Loss 4.2210 (4.2754)    Prec@1 31.250 (33.239)  Prec@5 62.500 (62.713)
Train Epoch: [5][21/47],lr: 0.00005 Loss 4.2603 (4.2594)    Prec@1 31.250 (32.626)  Prec@5 57.812 (61.793)
Train Epoch: [5][31/47],lr: 0.00005 Loss 4.2397 (4.2502)    Prec@1 29.688 (31.678)  Prec@5 62.500 (61.164)
Train Epoch: [5][41/47],lr: 0.00005 Loss 4.2377 (4.2285)    Prec@1 26.562 (31.155)  Prec@5 60.156 (61.300)
Train Epoch: [5][47/47],lr: 0.00005 Loss 4.1144 (4.2206)    Prec@1 34.906 (31.365)  Prec@5 59.434 (61.328)
Val Epoch: [5][1/46]    Loss 3.6491 (3.6491)    
Cls@1:0.398 Cls@5:0.789
Loc@1:0.203 Loc@5:0.445 Loc_gt:0.516

Val Epoch: [5][11/46]   Loss 3.5341 (4.0492)    
Cls@1:0.343 Cls@5:0.620
Loc@1:0.140 Loc@5:0.247 Loc_gt:0.333

Val Epoch: [5][21/46]   Loss 4.3516 (3.9736)    
Cls@1:0.353 Cls@5:0.653
Loc@1:0.156 Loc@5:0.279 Loc_gt:0.361

Val Epoch: [5][31/46]   Loss 4.5599 (4.1005)    
Cls@1:0.306 Cls@5:0.605
Loc@1:0.132 Loc@5:0.238 Loc_gt:0.318

Val Epoch: [5][41/46]   Loss 4.3230 (4.1360)    
Cls@1:0.286 Cls@5:0.593
Loc@1:0.121 Loc@5:0.225 Loc_gt:0.306

Val Epoch: [5][46/46]   Loss 4.1012 (4.1362)    
Cls@1:0.295 Cls@5:0.602
Loc@1:0.119 Loc@5:0.220 Loc_gt:0.297

wrong_details:688 4082 0 88 912 24
Best GT_LOC: 0.2965136347946151
Best TOP1_LOC: 0.2965136347946151
2022-03-25-02-02
Train Epoch: [6][1/47],lr: 0.00005  Loss 4.1231 (4.1231)    Prec@1 30.469 (30.469)  Prec@5 66.406 (66.406)
Train Epoch: [6][11/47],lr: 0.00005 Loss 4.0252 (4.0962)    Prec@1 37.500 (35.085)  Prec@5 70.312 (67.756)
Train Epoch: [6][21/47],lr: 0.00005 Loss 3.9509 (4.0630)    Prec@1 40.625 (36.533)  Prec@5 67.969 (67.671)
Train Epoch: [6][31/47],lr: 0.00005 Loss 3.8919 (4.0431)    Prec@1 45.312 (36.215)  Prec@5 64.844 (67.137)
Train Epoch: [6][41/47],lr: 0.00005 Loss 3.9957 (4.0417)    Prec@1 40.625 (36.128)  Prec@5 70.312 (66.749)
Train Epoch: [6][47/47],lr: 0.00005 Loss 3.9811 (4.0315)    Prec@1 41.509 (36.303)  Prec@5 64.151 (67.050)
Val Epoch: [6][1/46]    Loss 3.4300 (3.4300)    
Cls@1:0.438 Cls@5:0.781
Loc@1:0.219 Loc@5:0.453 Loc_gt:0.531

Val Epoch: [6][11/46]   Loss 3.3890 (3.8868)    
Cls@1:0.357 Cls@5:0.643
Loc@1:0.145 Loc@5:0.259 Loc_gt:0.343

Val Epoch: [6][21/46]   Loss 4.1725 (3.7921)    
Cls@1:0.377 Cls@5:0.680
Loc@1:0.170 Loc@5:0.299 Loc_gt:0.379

Val Epoch: [6][31/46]   Loss 4.4162 (3.9271)    
Cls@1:0.331 Cls@5:0.634
Loc@1:0.146 Loc@5:0.259 Loc_gt:0.336

Val Epoch: [6][41/46]   Loss 4.2253 (3.9698)    
Cls@1:0.313 Cls@5:0.623
Loc@1:0.136 Loc@5:0.245 Loc_gt:0.325

Val Epoch: [6][46/46]   Loss 3.9466 (3.9713)    
Cls@1:0.321 Cls@5:0.632
Loc@1:0.134 Loc@5:0.239 Loc_gt:0.314

wrong_details:776 3935 0 118 940 25
Best GT_LOC: 0.3139454608215395
Best TOP1_LOC: 0.3139454608215395
Preparing networks done!
Train Epoch: [1][1/47],lr: 0.00005  Loss 5.3910 (5.3910)    Prec@1 0.781 (0.781)    Prec@5 3.125 (3.125)
Train Epoch: [1][11/47],lr: 0.00005 Loss 5.2530 (5.3511)    Prec@1 1.562 (0.923)    Prec@5 3.906 (3.125)
Train Epoch: [1][21/47],lr: 0.00005 Loss 5.2631 (5.3216)    Prec@1 0.781 (0.781)    Prec@5 3.906 (3.646)
Train Epoch: [1][31/47],lr: 0.00005 Loss 5.1785 (5.2905)    Prec@1 0.781 (0.907)    Prec@5 3.125 (4.461)
Train Epoch: [1][41/47],lr: 0.00005 Loss 5.1472 (5.2599)    Prec@1 1.562 (0.953)    Prec@5 7.031 (4.668)
Train Epoch: [1][47/47],lr: 0.00005 Loss 5.1461 (5.2453)    Prec@1 1.887 (1.001)    Prec@5 12.264 (4.805)
Val Epoch: [1][1/46]    Loss 4.8300 (4.8300)    
Cls@1:0.000 Cls@5:0.094
Loc@1:0.000 Loc@5:0.023 Loc_gt:0.312

Val Epoch: [1][11/46]   Loss 4.7840 (5.0671)    
Cls@1:0.010 Cls@5:0.077
Loc@1:0.002 Loc@5:0.014 Loc_gt:0.224

Val Epoch: [1][21/46]   Loss 5.3839 (5.0786)    
Cls@1:0.010 Cls@5:0.070
Loc@1:0.002 Loc@5:0.016 Loc_gt:0.218

Val Epoch: [1][31/46]   Loss 5.3107 (5.1220)    
Cls@1:0.010 Cls@5:0.061
Loc@1:0.003 Loc@5:0.014 Loc_gt:0.199

Val Epoch: [1][41/46]   Loss 4.7929 (5.0675)    
Cls@1:0.016 Cls@5:0.069
Loc@1:0.005 Loc@5:0.016 Loc_gt:0.195

Val Epoch: [1][46/46]   Loss 5.0628 (5.0798)    
Cls@1:0.016 Cls@5:0.066
Loc@1:0.005 Loc@5:0.015 Loc_gt:0.192

wrong_details:27 5704 0 3 58 2
Best GT_LOC: 0.1924404556437694
Best TOP1_LOC: 0.1924404556437694
2022-03-25-01-49
Train Epoch: [2][1/47],lr: 0.00005  Loss 5.0344 (5.0344)    Prec@1 2.344 (2.344)    Prec@5 7.812 (7.812)
Train Epoch: [2][11/47],lr: 0.00005 Loss 4.9748 (5.0317)    Prec@1 0.781 (1.634)    Prec@5 9.375 (7.812)
Train Epoch: [2][21/47],lr: 0.00005 Loss 4.8753 (4.9973)    Prec@1 3.906 (2.083)    Prec@5 10.938 (8.557)
Train Epoch: [2][31/47],lr: 0.00005 Loss 4.8447 (4.9587)    Prec@1 1.562 (2.419)    Prec@5 9.375 (9.199)
Train Epoch: [2][41/47],lr: 0.00005 Loss 4.9252 (4.9204)    Prec@1 0.781 (2.591)    Prec@5 10.938 (10.061)
Train Epoch: [2][47/47],lr: 0.00005 Loss 4.7829 (4.8979)    Prec@1 4.717 (2.669)    Prec@5 16.981 (10.777)
Val Epoch: [2][1/46]    Loss 4.8567 (4.8567)    
Cls@1:0.008 Cls@5:0.109
Loc@1:0.008 Loc@5:0.078 Loc_gt:0.352

Val Epoch: [2][11/46]   Loss 4.4700 (4.4981)    
Cls@1:0.062 Cls@5:0.232
Loc@1:0.018 Loc@5:0.061 Loc_gt:0.241

Val Epoch: [2][21/46]   Loss 4.9166 (4.5804)    
Cls@1:0.057 Cls@5:0.199
Loc@1:0.015 Loc@5:0.049 Loc_gt:0.231

Val Epoch: [2][31/46]   Loss 4.8543 (4.6311)    
Cls@1:0.055 Cls@5:0.184
Loc@1:0.013 Loc@5:0.044 Loc_gt:0.230

Val Epoch: [2][41/46]   Loss 4.4745 (4.5779)    
Cls@1:0.052 Cls@5:0.182
Loc@1:0.013 Loc@5:0.046 Loc_gt:0.217

Val Epoch: [2][46/46]   Loss 3.8181 (4.5981)    
Cls@1:0.051 Cls@5:0.177
Loc@1:0.012 Loc@5:0.044 Loc_gt:0.215

wrong_details:71 5500 0 116 98 9
Best GT_LOC: 0.21453227476700035
Best TOP1_LOC: 0.21453227476700035
2022-03-25-01-54
Train Epoch: [3][1/47],lr: 0.00005  Loss 4.4202 (4.4202)    Prec@1 7.031 (7.031)    Prec@5 21.875 (21.875)
Train Epoch: [3][11/47],lr: 0.00005 Loss 4.2909 (4.5166)    Prec@1 8.594 (5.114)    Prec@5 26.562 (19.389)
Train Epoch: [3][21/47],lr: 0.00005 Loss 4.5282 (4.5042)    Prec@1 1.562 (5.320)    Prec@5 11.719 (19.085)
Train Epoch: [3][31/47],lr: 0.00005 Loss 4.3235 (4.4662)    Prec@1 9.375 (5.872)    Prec@5 21.875 (19.481)
Train Epoch: [3][41/47],lr: 0.00005 Loss 4.2087 (4.4195)    Prec@1 8.594 (6.250)    Prec@5 31.250 (21.418)
Train Epoch: [3][47/47],lr: 0.00005 Loss 4.1792 (4.3886)    Prec@1 7.547 (6.390)    Prec@5 22.642 (21.955)
Val Epoch: [3][1/46]    Loss 3.8698 (3.8698)    
Cls@1:0.172 Cls@5:0.289
Loc@1:0.055 Loc@5:0.102 Loc_gt:0.375

Val Epoch: [3][11/46]   Loss 4.1646 (3.9793)    
Cls@1:0.118 Cls@5:0.327
Loc@1:0.029 Loc@5:0.101 Loc_gt:0.257

Val Epoch: [3][21/46]   Loss 4.8005 (4.0862)    
Cls@1:0.106 Cls@5:0.299
Loc@1:0.022 Loc@5:0.076 Loc_gt:0.228

Val Epoch: [3][31/46]   Loss 4.6957 (4.3865)    
Cls@1:0.085 Cls@5:0.241
Loc@1:0.017 Loc@5:0.061 Loc_gt:0.225

Val Epoch: [3][41/46]   Loss 4.3491 (4.2824)    
Cls@1:0.082 Cls@5:0.253
Loc@1:0.017 Loc@5:0.062 Loc_gt:0.210

Val Epoch: [3][46/46]   Loss 3.9240 (4.3113)    
Cls@1:0.079 Cls@5:0.246
Loc@1:0.016 Loc@5:0.057 Loc_gt:0.203

wrong_details:90 5334 0 314 42 14
Best GT_LOC: 0.21453227476700035
Best TOP1_LOC: 0.21453227476700035
2022-03-25-02-00
Train Epoch: [4][1/47],lr: 0.00005  Loss 3.9914 (3.9914)    Prec@1 12.500 (12.500)  Prec@5 34.375 (34.375)
Train Epoch: [4][11/47],lr: 0.00005 Loss 4.1547 (4.0524)    Prec@1 10.156 (11.364)  Prec@5 29.688 (30.753)
Train Epoch: [4][21/47],lr: 0.00005 Loss 4.3899 (4.0732)    Prec@1 7.031 (10.528)   Prec@5 19.531 (30.432)
Train Epoch: [4][31/47],lr: 0.00005 Loss 3.7553 (4.0368)    Prec@1 11.719 (10.459)  Prec@5 38.281 (31.401)
Train Epoch: [4][41/47],lr: 0.00005 Loss 3.9345 (4.0095)    Prec@1 8.594 (10.690)   Prec@5 30.469 (31.879)
Train Epoch: [4][47/47],lr: 0.00005 Loss 3.7705 (3.9801)    Prec@1 16.981 (11.195)  Prec@5 38.679 (32.733)
Val Epoch: [4][1/46]    Loss 4.2226 (4.2226)    
Cls@1:0.141 Cls@5:0.344
Loc@1:0.086 Loc@5:0.195 Loc_gt:0.461

Val Epoch: [4][11/46]   Loss 3.6754 (3.8854)    
Cls@1:0.148 Cls@5:0.378
Loc@1:0.048 Loc@5:0.126 Loc_gt:0.303

Val Epoch: [4][21/46]   Loss 4.4785 (3.9334)    
Cls@1:0.134 Cls@5:0.353
Loc@1:0.036 Loc@5:0.102 Loc_gt:0.283

Val Epoch: [4][31/46]   Loss 4.5506 (3.9342)    
Cls@1:0.123 Cls@5:0.345
Loc@1:0.035 Loc@5:0.108 Loc_gt:0.294

Val Epoch: [4][41/46]   Loss 3.8479 (3.8611)    
Cls@1:0.121 Cls@5:0.354
Loc@1:0.030 Loc@5:0.100 Loc_gt:0.267

Val Epoch: [4][46/46]   Loss 3.7787 (3.8731)    
Cls@1:0.118 Cls@5:0.345
Loc@1:0.028 Loc@5:0.094 Loc_gt:0.265

wrong_details:164 5109 0 465 41 15
Best GT_LOC: 0.26458405246807043
Best TOP1_LOC: 0.26458405246807043
2022-03-25-02-02
Train Epoch: [5][1/47],lr: 0.00005  Loss 3.6695 (3.6695)    Prec@1 11.719 (11.719)  Prec@5 38.281 (38.281)
Train Epoch: [5][11/47],lr: 0.00005 Loss 3.5674 (3.5657)    Prec@1 19.531 (16.548)  Prec@5 40.625 (42.827)
Train Epoch: [5][21/47],lr: 0.00005 Loss 3.5742 (3.5837)    Prec@1 13.281 (16.481)  Prec@5 40.625 (43.043)
Train Epoch: [5][31/47],lr: 0.00005 Loss 3.5905 (3.5552)    Prec@1 19.531 (17.339)  Prec@5 44.531 (43.901)
Train Epoch: [5][41/47],lr: 0.00005 Loss 3.4860 (3.5470)    Prec@1 22.656 (17.492)  Prec@5 46.875 (44.284)
Train Epoch: [5][47/47],lr: 0.00005 Loss 3.6738 (3.5731)    Prec@1 13.208 (17.017)  Prec@5 38.679 (43.694)
Val Epoch: [5][1/46]    Loss 3.6581 (3.6581)    
Cls@1:0.133 Cls@5:0.398
Loc@1:0.039 Loc@5:0.164 Loc_gt:0.328

Val Epoch: [5][11/46]   Loss 3.4856 (3.7789)    
Cls@1:0.167 Cls@5:0.392
Loc@1:0.037 Loc@5:0.097 Loc_gt:0.261

Val Epoch: [5][21/46]   Loss 3.7290 (3.6464)    
Cls@1:0.188 Cls@5:0.424
Loc@1:0.036 Loc@5:0.093 Loc_gt:0.240

Val Epoch: [5][31/46]   Loss 4.2331 (3.7345)    
Cls@1:0.165 Cls@5:0.397
Loc@1:0.036 Loc@5:0.094 Loc_gt:0.242

Val Epoch: [5][41/46]   Loss 4.0443 (3.7508)    
Cls@1:0.156 Cls@5:0.393
Loc@1:0.034 Loc@5:0.089 Loc_gt:0.227

Val Epoch: [5][46/46]   Loss 3.5688 (3.7679)    
Cls@1:0.150 Cls@5:0.389
Loc@1:0.032 Loc@5:0.086 Loc_gt:0.226

wrong_details:185 4926 0 622 34 27
Best GT_LOC: 0.26458405246807043
Best TOP1_LOC: 0.26458405246807043

Looking for your help!

vasgaowei commented 2 years ago

If training model under the same setting as paper, the result should not differ a lot. Could you try running the code on one or two GPUs?