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Question about the training process #14

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Unrealluver opened this issue Apr 20, 2022 · 1 comment
Open

Question about the training process #14

Unrealluver opened this issue Apr 20, 2022 · 1 comment

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@Unrealluver
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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
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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?

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@vasgaowei @Unrealluver