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This repo consist of some experimental results on bdd100k datasets using different object detection algorithms(Faster-RCNN, FCOS, ATSS)

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TengFeiHan0/Object-Detection.pytorch

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Object-Detection.pytorch

bdd100k Dataset Baseline

  • we use mmdetection to train all models.
  • All models were trained on bdd100k_train, and tested on the bdd100k_val.
  • We use distributed training across 8 Nvdia-1080Ti GPUs.

Anchor-based:

Name backbone tricks AP AP50 AP75 APs APm APl
FasterRCNN R_50_FPN 0.318 0.551 0.311 0.145 0.356 0.497
FasterRCNN R_101_FPN 0.322 0.553 0.314 0.142 0.360 0.512
CascadeRCNN R_50_FPN 0.332 0.558 0.331 0.150 0.371 0.520
PISA R_50_FPN
LibraRCNN R_50_FPN
GA R_50_FPN

Anchor-free

Name backbone tricks AP AP50 AP75 APs APm APl
FCOS R_50_FPN 0.304 0.539 0.290 0.129 0.338 0.498
ATSS R_50_FPN 0.329 0.562 0.323 0.141 0.367 0.517
CenterNet R_50_DCN
RepPoints R_50_FPN 0.312 0.555 0.297 0.129 0.348 0.505

CenterNet series

Name backbone Iters AP AP50 AP75 APs APm APl
CenterNet R_50_DCN 125997 27.5269 44.7613 28.8301 9.6805 31.4682 43.1641

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This repo consist of some experimental results on bdd100k datasets using different object detection algorithms(Faster-RCNN, FCOS, ATSS)

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