computer-vision
Robust Object Detection
3260 papers • 126 benchmarks • 313 datasets
A Benchmark for the: Robustness of Object Detection Models to Image Corruptions and Distortions To allow fair comparison of robustness enhancing methods all models have to use a standard ResNet50 backbone because performance strongly scales with backbone capacity. If requested an unrestricted category can be added later. Benchmark Homepage: https://github.com/bethgelab/robust-detection-benchmark Metrics: mPC [AP]: Mean Performance under Corruption [measured in AP] rPC [%]: Relative Performance under Corruption [measured in %] Test sets: Coco: val 2017; Pascal VOC: test 2007; Cityscapes: val; ( Image credit: Benchmarking Robustness in Object Detection )
(Image credit: Papersgraph)
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These leaderboards are used to track progress in robust-object-detection
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Most implemented papers
YOLOv3: An Incremental Improvement
Ali Farhadi, Joseph Redmon•Sat Apr 07 2018
We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at this https URL
