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DensePose Model Zoo

RPN Files

Herein, we provide RPN files for DensePose-COCO dataset train, minival and valminusminival partitions.

The RPN results are obtained using the models provided in the Detectron model-zoo. For performance measures please refer to this file.

X-101-32x8d-FPN: [train] [minival] [valminusminival]

R-50-FPN: [train] [minival] [valminusminival]

DensePose-RCNN Models

Model AP AP50 AP75 APm APl
ResNet50_FPN_s1x 0.4748 0.8368 0.4820 0.4262 0.4948
ResNet50_FPN_s1x-e2e 0.4892 0.8490 0.5078 0.4384 0.5059
ResNet101_FPN_s1x 0.4978 0.8521 0.5276 0.4373 0.5164
ResNet101_FPN_s1x-e2e 0.5147 0.8660 0.5601 0.4716 0.5291
ResNet101_FPN_32x8d_s1x 0.5095 0.8590 0.5381 0.4605 0.5272
ResNet101_FPN_32x8d_s1x-e2e 0.5554 0.8908 0.6080 0.5067 0.5676

Please note that due to the new per-part normalization the AP numbers do not match those reported in the paper, which are obtained with global normalization factor K = 0.255.

Models with Multiple Heads

We provide an example of a configuration file that performs multiple tasks using the same backbone architecture (ResNet-50) and containing several heads for dense pose, mask and keypoints estimation. We note that this example is provided purely for illustrative purposes and the performance of the model is not tuned. As an alternative, one can always use independent models for individual tasks.

Task AP AP50 AP75 APm APl
mask 0.4903 0.8160 0.5300 0.4379 0.6417
keypoint 0.6159 0.8614 0.6665 0.4847 0.7233
densepose 0.5075 0.8606 0.5373 0.4356 0.5265

(config, model, md5)