TS-CAM
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Published
20 hours ago •
vasgaowei
vasgaowei
Question about the training process
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!
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?
