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End-to-End Object Detection with Transformers
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Very low performance for segmentation task. #613

Open lianzheng-research opened 7 months ago

lianzheng-research commented 7 months ago

Thanks for your excellent work!

I have pretrained DETR on COCO dataset with command:

CUDA_VISIBLE_DEVICES=4,5,6,7 python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py --coco_path ../datasets/coco/ --batch_size 6 --world_size 4 --output_dir outputs/detr-coco-20231028/box_model/

And now I'm trying to finetune it on segmentation task with command:

CUDA_VISIBLE_DEVICES=4,5,6,7 python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py --masks --epochs 25 --lr_drop 15 --coco_path ../datasets/coco/ --coco_panoptic_path ../datasets/coco_panoptic/ --dataset_file coco_panoptic --frozen_weights outputs/detr-coco-20231028/box_model/checkpoint.pth --output_dir outputs/detr-coco-20231028/segm/ --batch_size 8 --world_size 4

But my loss is not decreasing and maintains around 45 points from the beginning to the end. The class error has the same behavior.

Epoch: [0]  [   0/3696]  eta: 6:24:44  lr: 0.000100  class_error: 75.10  loss: 45.1652 (45.1652)  loss_bbox: 1.1062 (1.1062)  loss_bbox_0: 1.0980 (1.0980)  loss_bbox_1: 1.0900 (1.0900)  loss_bbox_2: 1.1241 (1.1241)  loss_bbox_3: 1.1097 (1.1097)  loss_bbox_4: 1.0968 (1.0968)  loss_ce: 5.2375 (5.2375)  loss_ce_0: 5.1831 (5.1831)  loss_ce_1: 5.2124 (5.2124)  loss_ce_2: 5.2343 (5.2343)  loss_ce_3: 5.2243 (5.2243)  loss_ce_4: 5.2440 (5.2440)  loss_dice: 0.9501 (0.9501)  loss_giou: 1.0081 (1.0081)  loss_giou_0: 1.0500 (1.0500)  loss_giou_1: 1.0273 (1.0273)  loss_giou_2: 1.0172 (1.0172)  loss_giou_3: 1.0156 (1.0156)  loss_giou_4: 1.0123 (1.0123)  loss_mask: 0.1242 (0.1242)  cardinality_error_unscaled: 88.5625 (88.5625)  cardinality_error_0_unscaled: 88.5625 (88.5625)  cardinality_error_1_unscaled: 88.5625 (88.5625)  cardinality_error_2_unscaled: 88.5625 (88.5625)  cardinality_error_3_unscaled: 88.5625 (88.5625)  cardinality_error_4_unscaled: 88.5625 (88.5625)  class_error_unscaled: 75.0977 (75.0977)  loss_bbox_unscaled: 0.2212 (0.2212)  loss_bbox_0_unscaled: 0.2196 (0.2196)  loss_bbox_1_unscaled: 0.2180 (0.2180)  loss_bbox_2_unscaled: 0.2248 (0.2248)  loss_bbox_3_unscaled: 0.2219 (0.2219)  loss_bbox_4_unscaled: 0.2194 (0.2194)  loss_ce_unscaled: 5.2375 (5.2375)  loss_ce_0_unscaled: 5.1831 (5.1831)  loss_ce_1_unscaled: 5.2124 (5.2124)  loss_ce_2_unscaled: 5.2343 (5.2343)  loss_ce_3_unscaled: 5.2243 (5.2243)  loss_ce_4_unscaled: 5.2440 (5.2440)  loss_dice_unscaled: 0.9501 (0.9501)  loss_giou_unscaled: 0.5041 (0.5041)  loss_giou_0_unscaled: 0.5250 (0.5250)  loss_giou_1_unscaled: 0.5137 (0.5137)  loss_giou_2_unscaled: 0.5086 (0.5086)  loss_giou_3_unscaled: 0.5078 (0.5078)  loss_giou_4_unscaled: 0.5062 (0.5062)  loss_mask_unscaled: 0.1242 (0.1242)  time: 6.2459  data: 2.2261  max mem: 11054
...
Epoch: [24]  [3695/3696]  eta: 0:00:04  lr: 0.000010  class_error: 75.27  loss: 45.3449 (44.8820)  loss_bbox: 1.1708 (1.1440)  loss_bbox_0: 1.1676 (1.1385)  loss_bbox_1: 1.1531 (1.1288)  loss_bbox_2: 1.1777 (1.1487)  loss_bbox_3: 1.1720 (1.1482)  loss_bbox_4: 1.1655 (1.1405)  loss_ce: 5.3088 (5.3016)  loss_ce_0: 5.2577 (5.2610)  loss_ce_1: 5.3025 (5.2981)  loss_ce_2: 5.3195 (5.3152)  loss_ce_3: 5.2854 (5.2848)  loss_ce_4: 5.3072 (5.2983)  loss_dice: 0.4689 (0.4755)  loss_giou: 0.9462 (0.9466)  loss_giou_0: 0.9980 (0.9972)  loss_giou_1: 0.9560 (0.9649)  loss_giou_2: 0.9686 (0.9571)  loss_giou_3: 0.9512 (0.9525)  loss_giou_4: 0.9495 (0.9480)  loss_mask: 0.0307 (0.0324)  cardinality_error_unscaled: 89.6562 (89.5641)  cardinality_error_0_unscaled: 89.6562 (89.5627)  cardinality_error_1_unscaled: 89.6562 (89.5637)  cardinality_error_2_unscaled: 89.6562 (89.5635)  cardinality_error_3_unscaled: 89.6562 (89.5636)  cardinality_error_4_unscaled: 89.6562 (89.5639)  class_error_unscaled: 79.0230 (77.3168)  loss_bbox_unscaled: 0.2342 (0.2288)  loss_bbox_0_unscaled: 0.2335 (0.2277)  loss_bbox_1_unscaled: 0.2306 (0.2258)  loss_bbox_2_unscaled: 0.2355 (0.2297)  loss_bbox_3_unscaled: 0.2344 (0.2296)  loss_bbox_4_unscaled: 0.2331 (0.2281)  loss_ce_unscaled: 5.3088 (5.3016)  loss_ce_0_unscaled: 5.2577 (5.2610)  loss_ce_1_unscaled: 5.3025 (5.2981)  loss_ce_2_unscaled: 5.3195 (5.3152)  loss_ce_3_unscaled: 5.2854 (5.2848)  loss_ce_4_unscaled: 5.3072 (5.2983)  loss_dice_unscaled: 0.4689 (0.4755)  loss_giou_unscaled: 0.4731 (0.4733)  loss_giou_0_unscaled: 0.4990 (0.4986)  loss_giou_1_unscaled: 0.4780 (0.4824)  loss_giou_2_unscaled: 0.4843 (0.4786)  loss_giou_3_unscaled: 0.4756 (0.4763)  loss_giou_4_unscaled: 0.4748 (0.4740)  loss_mask_unscaled: 0.0307 (0.0324)  time: 4.1762  data: 2.0446  max mem: 24500

After finetuning on COCO panoptic dataset for 25 epochs, I got the following scores:

Accumulating evaluation results...
DONE (t=16.20s).
Accumulating evaluation results...
DONE (t=28.51s).
IoU metric: segm
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.026
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.051
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.024
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.069
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.051
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.083
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.099
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.101
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.190
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.039
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.064
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.039
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.046
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.090
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.065
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.108
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.130
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.040
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.127
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.240

...

          |    PQ     SQ     RQ     N
--------------------------------------
All       |   0.0    0.0    0.0   133
Things    |   0.0    0.0    0.0    80
Stuff     |   0.0    0.0    0.0    53

I need some help. Why did I get such low scores? Thank you very much!