Papers › Cascade R-CNN: Delving into High Quality Object Detection
Cascade R-CNN: Delving into High Quality Object Detection
Zhaowei Cai, Nuno Vasconcelos
In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code will be made available at https://github.com/zhaoweicai/cascade-rcnn.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 2D Object Detection | SARDet-100K | Cascade R-CNN | box mAP | 51.1 | #6 of 13 | Archive leaderboard | report |
| Object Detection | AI-TOD | Cascade R-CNN (ResNet-50-FPN) | AP | 13.8 | #6 of 7 | Archive leaderboard | report |
| Object Detection | AI-TOD | Cascade R-CNN (ResNet-50-FPN) | AP50 | 30.8 | #6 of 7 | Archive leaderboard | report |
| Object Detection | AI-TOD | Cascade R-CNN (ResNet-50-FPN) | AP75 | 10.5 | #6 of 7 | Archive leaderboard | report |
| Object Detection | AI-TOD | Cascade R-CNN (ResNet-50-FPN) | APm | 26.6 | #6 of 7 | Archive leaderboard | report |
| Object Detection | AI-TOD | Cascade R-CNN (ResNet-50-FPN) | APs | 25.5 | #6 of 7 | Archive leaderboard | report |
| Object Detection | AI-TOD | Cascade R-CNN (ResNet-50-FPN) | APt | 10.6 | #6 of 7 | Archive leaderboard | report |
| Object Detection | AI-TOD | Cascade R-CNN (ResNet-50-FPN) | APvt | 0.0 | #6 of 7 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-101-FPN+, cascade) | AP50 | 61.6 | #150 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-101-FPN+, cascade) | AP75 | 46.6 | #150 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-101-FPN+, cascade) | APL | 57.4 | #150 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-101-FPN+, cascade) | APM | 46.2 | #150 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-101-FPN+, cascade) | APS | 23.8 | #150 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-101-FPN+, cascade) | box AP | 42.7 | #150 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-50-FPN+) | AP50 | 59.4 | #180 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-50-FPN+) | AP75 | 43.7 | #180 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-50-FPN+) | APL | 54.1 | #180 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-50-FPN+) | APM | 43.7 | #180 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-50-FPN+) | APS | 22.9 | #180 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade R-CNN (ResNet-50-FPN+) | box AP | 40.3 | #180 of 220 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+, cascade) | AP50 | 62.1 | #168 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+, cascade) | AP75 | 46.3 | #168 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+, cascade) | APL | 55.2 | #168 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+, cascade) | APM | 45.5 | #168 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+, cascade) | APS | 23.7 | #168 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+, cascade) | box mAP | 42.8 | #168 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+, cascade) | AP50 | 59.9 | #189 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+, cascade) | AP75 | 44 | #189 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+, cascade) | APL | 52.1 | #189 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+, cascade) | APM | 42.7 | #189 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+, cascade) | APS | 22.6 | #189 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+, cascade) | Hardware Burden | 12G | #189 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+, cascade) | box mAP | 40.6 | #189 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+) | AP50 | 61.1 | #209 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+) | AP75 | 41.9 | #209 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+) | APL | 49.8 | #209 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+) | APM | 41.8 | #209 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+) | APS | 21.3 | #209 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+) | Hardware Burden | 3G | #209 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-101-FPN+) | box mAP | 38.8 | #209 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+) | AP50 | 59 | #222 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+) | AP75 | 39.2 | #222 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+) | APL | 46.4 | #222 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+) | APM | 38.8 | #222 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+) | APS | 20.3 | #222 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+) | Hardware Burden | 3G | #222 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade R-CNN (ResNet-50-FPN+) | box mAP | 36.5 | #222 of 225 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Cascade Mask R-CNN, Cascade R-CNN
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