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DETR COCO val5k eval

DETR COCO 2017 evaluation results

Object detection

DETR R50

Train command line for a single node training:

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
    --lr_drop 400 --epochs 500 \
    --coco_path /path/to/coco

Eval command line:

python main.py --batch_size 2 --no_aux_loss --eval \
    --resume https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth \
    --coco_path /path/to/coco

COCO bbox detection val5k evaluation results:

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.420
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.624
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.442
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.205
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.458
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.333
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.533
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.574
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.312
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.628
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.805

DETR R50-DC5

Train command line for training on 8 nodes:

python run_with_submitit.py \
    --nodes 8 --timeout 3200 \
    --batch_size 1 --dilation \
    --lr_drop 400 --epochs 500 \
    --coco_path /path/to/coco

Eval command line:

python main.py --no_aux_loss --eval \
    --batch_size 1 --dilation \
    --resume https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-f0fb7ef5.pth \
    --coco_path /path/to/coco

COCO bbox detection val5k evaluation results:

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.433
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.631
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.459
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.225
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.473
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.611
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.342
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.551
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.594
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.344
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.646
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.814

DETR R101

Train command line for a single node training:

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
    --backbone resnet101 \
    --lr_drop 400 --epochs 500 \
    --coco_path /path/to/coco

Eval command line:

python main.py --batch_size 2 --no_aux_loss --eval \
    --backbone resnet101 \
    --resume https://dl.fbaipublicfiles.com/detr/detr-r101-2c7b67e5.pth \
    --coco_path /path/to/coco

COCO bbox detection val5k evaluation results:

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.435
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.638
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.464
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.219
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.480
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.344
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.548
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.590
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.337
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.644
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.814

DETR R101-DC5

Train command line for training on 8 nodes:

python run_with_submitit.py \
    --nodes 8 --timeout 3200 \
    --backbone resnet101 \
    --batch_size 1 --dilation \
    --lr_drop 400 --epochs 500 \
    --coco_path /path/to/coco

Eval command line:

python main.py --no_aux_loss --eval \
    --backbone resnet101 \
    --batch_size 1 --dilation \
    --resume https://dl.fbaipublicfiles.com/detr/detr-r101-dc5-a2e86def.pth \
    --coco_path /path/to/coco

COCO bbox detection val5k evaluation results:

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.449
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.647
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.477
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.623
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.350
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.561
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.604
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.348
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.662
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.810

Panoptic segmentation

DETR R50

Eval command line:

python main.py \
    --batch_size 1 --no_aux_loss --eval \
    --resume https://dl.fbaipublicfiles.com/detr/detr-r50-panoptic-00ce5173.pth \
    --masks --dataset_file coco_panoptic \
    --coco_path /path/to/coco/ \
    --coco_panoptic_path /path/to/coco_panoptic

Results:

IoU metric: segm
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.311
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.541
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.313
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.116
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.346
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.507
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.264
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.395
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.411
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.467
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.388
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.599
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.400
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.173
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.428
 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 ] = 0.314
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.485
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.510
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.253
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.561
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.738
          |    PQ     SQ     RQ     N
--------------------------------------
All       |  43.4   79.3   53.8   133
Things    |  48.2   79.8   59.5    80
Stuff     |  36.3   78.5   45.3    53

DETR-R50 DC5

Eval command line:

python main.py \
    --dilation \
    --batch_size 1 --no_aux_loss --eval \
    --resume https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-panoptic-da08f1b1.pth \
    --masks --dataset_file coco_panoptic \
    --coco_path /path/to/coco/ \
    --coco_panoptic_path /path/to/coco_panoptic

Results:

IoU metric: segm
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.319
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.547
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.325
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.358
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.268
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.406
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.424
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.210
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.478
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.609
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.402
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.601
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.418
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.193
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.441
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.592
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.499
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.527
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.279
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.577
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.743
          |    PQ     SQ     RQ     N
--------------------------------------
All       |  44.6   79.8   55.0   133
Things    |  49.4   80.5   60.6    80
Stuff     |  37.3   78.7   46.5    53

DETR-R101

Eval command line:

python main.py \
    --backbone resnet101 \
    --batch_size 1 --no_aux_loss --eval \
    --resume https://dl.fbaipublicfiles.com/detr/detr-r101-panoptic-40021d53.pth \
    --masks --dataset_file coco_panoptic \
    --coco_path /path/to/coco/ \
    --coco_panoptic_path /path/to/coco_panoptic

Results:

IoU metric: segm
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.330
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.565
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.337
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.371
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.524
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.276
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.417
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.434
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.216
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.631
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.401
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.611
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.419
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.184
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.441
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.592
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.321
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.498
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.522
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.270
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.574
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.750
          |    PQ     SQ     RQ     N
--------------------------------------
All       |  45.1   79.9   55.5   133
Things    |  50.5   80.9   61.7    80
Stuff     |  37.0   78.5   46.0    53
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