cmd_line.sh

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --lr_drop 100 --epochs 150

eval_25935349.txt

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.395
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.603
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.414
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.175
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.430
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.591
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.323
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.516
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.559
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.284
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.615
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.807

log_25935349.txt

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I am trying to rerun this, since I have only a notebook with one RTX3060 GPU, so I run the command as this:
python -m torch.distributed.launch --nproc_per_node=1 --use_env main.py --coco_path /home/swy/COCO2017
It nearly need 5 hours to run one epoch, so here I just got 4 epochs train log, but it is very different from the logs you list here, can you help to analyze where is my error?
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my setup args as this:
Namespace(aux_loss=True, backbone='resnet50', batch_size=1, bbox_loss_coef=5, clip_max_norm=0.1, coco_panoptic_path=None, coco_path='/home/swy/COCO2017', dataset_file='coco', dec_layers=6, device='cuda', dice_loss_coef=1, dilation=False, dim_feedforward=2048, dist_backend='nccl', dist_url='env://', distributed=True, dropout=0.1, enc_layers=6, eos_coef=0.1, epochs=300, eval=False, frozen_weights=None, giou_loss_coef=2, gpu=0, hidden_dim=256, lr=0.0001, lr_backbone=1e-05, lr_drop=200, mask_loss_coef=1, masks=False, nheads=8, num_queries=100, num_workers=2, output_dir='./outputs', position_embedding='sine', pre_norm=False, rank=0, remove_difficult=False, resume='', seed=42, set_cost_bbox=5, set_cost_class=1, set_cost_giou=2, start_epoch=0, weight_decay=0.0001, world_size=1)
number of params: 41302368

I am trying to rerun this, since I have only a notebook with one RTX3060 GPU, so I run the command as this: python -m torch.distributed.launch --nproc_per_node=1 --use_env main.py --coco_path /home/swy/COCO2017 It nearly need 5 hours to run one epoch, so here I just got 4 epochs train log, but it is very different from the logs you list here, can you help to analyze where is my error? {"train_lr": 9.999999999987971e-05, "train_class_error": 99.9982608031272, "train_loss": 28.02340816979022, "train_loss_ce": 1.8334115313894788, "train_loss_bbox": 1.3777571934501351, "train_loss_giou": 1.4179909137759255, "train_loss_ce_0": 1.856714030625626, "train_loss_bbox_0": 1.4015496531844143, "train_loss_giou_0": 1.4302295871598047, "train_loss_ce_1": 1.846910264853441, "train_loss_bbox_1": 1.422249663738627, "train_loss_giou_1": 1.4443229208683943, "train_loss_ce_2": 1.839646412516055, "train_loss_bbox_2": 1.4085562484294174, "train_loss_giou_2": 1.4346405850664319, "train_loss_ce_3": 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my setup args as this: Namespace(aux_loss=True, backbone='resnet50', batch_size=1, bbox_loss_coef=5, clip_max_norm=0.1, coco_panoptic_path=None, coco_path='/home/swy/COCO2017', dataset_file='coco', dec_layers=6, device='cuda', dice_loss_coef=1, dilation=False, dim_feedforward=2048, dist_backend='nccl', dist_url='env://', distributed=True, dropout=0.1, enc_layers=6, eos_coef=0.1, epochs=300, eval=False, frozen_weights=None, giou_loss_coef=2, gpu=0, hidden_dim=256, lr=0.0001, lr_backbone=1e-05, lr_drop=200, mask_loss_coef=1, masks=False, nheads=8, num_queries=100, num_workers=2, output_dir='./outputs', position_embedding='sine', pre_norm=False, rank=0, remove_difficult=False, resume='', seed=42, set_cost_bbox=5, set_cost_class=1, set_cost_giou=2, start_epoch=0, weight_decay=0.0001, world_size=1) number of params: 41302368

I get the same problem, the 'train_class_error' is 100 and never decreases.

I am trying to rerun this, since I have only a notebook with one RTX3060 GPU, so I run the command as this: python -m torch.distributed.launch --nproc_per_node=1 --use_env main.py --coco_path /home/swy/COCO2017 It nearly need 5 hours to run one epoch, so here I just got 4 epochs train log, but it is very different from the logs you list here, can you help to analyze where is my error? {"train_lr": 9.999999999987971e-05, "train_class_error": 99.9982608031272, "train_loss": 28.02340816979022, "train_loss_ce": 1.8334115313894788, "train_loss_bbox": 1.3777571934501351, "train_loss_giou": 1.4179909137759255, "train_loss_ce_0": 1.856714030625626, "train_loss_bbox_0": 1.4015496531844143, "train_loss_giou_0": 1.4302295871598047, "train_loss_ce_1": 1.846910264853441, "train_loss_bbox_1": 1.422249663738627, "train_loss_giou_1": 1.4443229208683943, "train_loss_ce_2": 1.839646412516055, "train_loss_bbox_2": 1.4085562484294174, "train_loss_giou_2": 1.4346405850664319, "train_loss_ce_3": 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"train_loss_giou_2_unscaled": 0.7173202925332159, "train_cardinality_error_2_unscaled": 6.579776306779274, "train_loss_ce_3_unscaled": 1.8383748121284051, "train_loss_bbox_3_unscaled": 0.27974942784299733, "train_loss_giou_3_unscaled": 0.7152757689190391, "train_cardinality_error_3_unscaled": 6.5795903184627225, "train_loss_ce_4_unscaled": 1.8420934639734754, "train_loss_bbox_4_unscaled": 0.275424672022656, "train_loss_giou_4_unscaled": 0.7112694423061307, "train_cardinality_error_4_unscaled": 6.577054114146102, "test_class_error": 100.0, "test_loss": 62.81850857815743, "test_loss_ce": 2.2845416783452035, "test_loss_bbox": 7.6273719230055805, "test_loss_giou": 1.6970758557677268, "test_loss_ce_0": 1.9679082337617875, "test_loss_bbox_0": 4.671925865781307, "test_loss_giou_0": 2.9563303895235062, "test_loss_ce_1": 1.974536807602644, "test_loss_bbox_1": 4.572709416306019, "test_loss_giou_1": 2.8994841971635816, "test_loss_ce_2": 2.1613146026313306, "test_loss_bbox_2": 7.5489953542768955, 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my setup args as this: Namespace(aux_loss=True, backbone='resnet50', batch_size=1, bbox_loss_coef=5, clip_max_norm=0.1, coco_panoptic_path=None, coco_path='/home/swy/COCO2017', dataset_file='coco', dec_layers=6, device='cuda', dice_loss_coef=1, dilation=False, dim_feedforward=2048, dist_backend='nccl', dist_url='env://', distributed=True, dropout=0.1, enc_layers=6, eos_coef=0.1, epochs=300, eval=False, frozen_weights=None, giou_loss_coef=2, gpu=0, hidden_dim=256, lr=0.0001, lr_backbone=1e-05, lr_drop=200, mask_loss_coef=1, masks=False, nheads=8, num_queries=100, num_workers=2, output_dir='./outputs', position_embedding='sine', pre_norm=False, rank=0, remove_difficult=False, resume='', seed=42, set_cost_bbox=5, set_cost_class=1, set_cost_giou=2, start_epoch=0, weight_decay=0.0001, world_size=1) number of params: 41302368

Same here... ~3 hours per epoch on a single 2080ti and train_class_error stuck on 100

Hi did you find any fixes for this class_error being stuck at some value ? I'm facing the same issue training on custom dataset.,

Hi, same here I got stuck on class_error

Hello, I got stuck on class_error=100, how to solve it ?

Try finetuning, it is the only way I found it to be training on custom dataset, for some reason it simply isn't training on custom dataset from scratch, possibly has something to do with the small sizes of the custom dataset.

Hello, I got stuck on class_error=100, how to solve it ?

Consider the normalization functions that your model uses when an input image is given. By default, the normalization used is this (from ImageNet):
https://stackoverflow.com/questions/58151507/why-pytorch-officially-use-mean-0-485-0-456-0-406-and-std-0-229-0-224-0-2

If the mean color values of your custom datasets are significantly different, you will run into issues by using a backbone trained on ImageNet. Perhaps try calculating the mean & std. color values of your own dataset and set your own normalization function. For example:
https://github.com/andics/maskrcnn-combined-build/blob/master/dataset_processing/calculate_dataset_statistics/calculate_dataset_statistics.py

Check that you are using a large enough batch size, especially if you altered --nproc_per_node=8

I am trying to rerun this, since I have only a notebook with one RTX3060 GPU, so I run the command as this: python -m torch.distributed.launch --nproc_per_node=1 --use_env main.py --coco_path /home/swy/COCO2017 It nearly need 5 hours to run one epoch, so here I just got 4 epochs train log, but it is very different from the logs you list here, can you help to analyze where is my error? {"train_lr": 9.999999999987971e-05, "train_class_error": 99.9982608031272, "train_loss": 28.02340816979022, "train_loss_ce": 1.8334115313894788, "train_loss_bbox": 1.3777571934501351, "train_loss_giou": 1.4179909137759255, "train_loss_ce_0": 1.856714030625626, "train_loss_bbox_0": 1.4015496531844143, "train_loss_giou_0": 1.4302295871598047, "train_loss_ce_1": 1.846910264853441, "train_loss_bbox_1": 1.422249663738627, "train_loss_giou_1": 1.4443229208683943, "train_loss_ce_2": 1.839646412516055, "train_loss_bbox_2": 1.4085562484294174, "train_loss_giou_2": 1.4346405850664319, "train_loss_ce_3": 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my setup args as this: Namespace(aux_loss=True, backbone='resnet50', batch_size=1, bbox_loss_coef=5, clip_max_norm=0.1, coco_panoptic_path=None, coco_path='/home/swy/COCO2017', dataset_file='coco', dec_layers=6, device='cuda', dice_loss_coef=1, dilation=False, dim_feedforward=2048, dist_backend='nccl', dist_url='env://', distributed=True, dropout=0.1, enc_layers=6, eos_coef=0.1, epochs=300, eval=False, frozen_weights=None, giou_loss_coef=2, gpu=0, hidden_dim=256, lr=0.0001, lr_backbone=1e-05, lr_drop=200, mask_loss_coef=1, masks=False, nheads=8, num_queries=100, num_workers=2, output_dir='./outputs', position_embedding='sine', pre_norm=False, rank=0, remove_difficult=False, resume='', seed=42, set_cost_bbox=5, set_cost_class=1, set_cost_giou=2, start_epoch=0, weight_decay=0.0001, world_size=1) number of params: 41302368

Reduce your learning rate by 1/8, i.e., --lr 0.125e-4 --lr_backbone 0.125e-5. This is because his batch size is 2x8, yours is 2x1.

Reduce your learning rate by 1/8, i.e., --lr 0.125e-4 --lr_backbone 0.125e-5. This is because his batch size is 2x8, yours is 2x1.

Only your shortest answer resolve my all error. Thank you so much!