Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/Cascade R-CNN

Cascade R-CNN

Reported on 46 benchmarks across 5 tasks · 3 papers · 1 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology37 results

2D Object DetectiononSARDet-100K
box mAP· 2017-12-03
51.1
best: 55.4 (DenoDet)
SOTA
Cascade R-CNN: Delving into High Quality Object Detection arXiv:1712.00726
3DonManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
67.6
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
3DonUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
48.1
best: 52.1 (UniverseNet-20.08)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
3DonWaymo 2D detection all_ns f0val
COCO-style AP· uses extra data· 2021-03-25
36.4
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D ClassificationonManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
67.6
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D ClassificationonUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
48.1
best: 52.1 (UniverseNet-20.08)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D ClassificationonWaymo 2D detection all_ns f0val
COCO-style AP· uses extra data· 2021-03-25
36.4
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D Object DetectiononManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
67.6
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D Object DetectiononUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
48.1
best: 52.1 (UniverseNet-20.08)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D Object DetectiononWaymo 2D detection all_ns f0val
COCO-style AP· uses extra data· 2021-03-25
36.4
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
16konManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
67.6
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
16konUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
48.1
best: 52.1 (UniverseNet-20.08)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
16konWaymo 2D detection all_ns f0val
COCO-style AP· uses extra data· 2021-03-25
36.4
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
3DonCOCO test-dev
AP50· 2019-06-24
62.1
best: 95 (ViTPose (ViTAE-G, ensemble))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
3DonCOCO test-dev
AP75· 2019-06-24
46.3
best: 88.2 (ViTPose (ViTAE-G, ensemble))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
3DonCOCO test-dev
APL· 2019-06-24
55.2
best: 86.5 (PoseBH-H)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
3DonCOCO test-dev
APM· 2019-06-24
45.5
best: 83.8 (4xRSN-50 (ensemble))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
3DonCOCO test-dev
APS· 2019-06-24
23.7
best: 48.6 (Focal-Stable-DINO (Focal-Huge, no TTA))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
3DonCOCO test-dev
box mAP· 2019-06-24
42.8
best: 66 (Co-DETR)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D ClassificationonCOCO test-dev
AP50· 2019-06-24
62.1
best: 82.1 (Plain-DETR (Swin-L))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D ClassificationonCOCO test-dev
AP75· 2019-06-24
46.3
best: 71.7 (EVA)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D ClassificationonCOCO test-dev
APL· 2019-06-24
55.2
best: 78 (Focal-Stable-DINO (Focal-Huge, no TTA))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D ClassificationonCOCO test-dev
APM· 2019-06-24
45.5
best: 67.7 (EVA)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D ClassificationonCOCO test-dev
APS· 2019-06-24
23.7
best: 48.6 (Focal-Stable-DINO (Focal-Huge, no TTA))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D ClassificationonCOCO test-dev
box mAP· 2019-06-24
42.8
best: 66 (Co-DETR)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D Object DetectiononCOCO test-dev
AP50· 2019-06-24
62.1
best: 82.1 (Plain-DETR (Swin-L))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D Object DetectiononCOCO test-dev
AP75· 2019-06-24
46.3
best: 71.7 (EVA)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D Object DetectiononCOCO test-dev
APL· 2019-06-24
55.2
best: 78 (Focal-Stable-DINO (Focal-Huge, no TTA))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D Object DetectiononCOCO test-dev
APM· 2019-06-24
45.5
best: 67.7 (EVA)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D Object DetectiononCOCO test-dev
APS· 2019-06-24
23.7
best: 48.6 (Focal-Stable-DINO (Focal-Huge, no TTA))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
2D Object DetectiononCOCO test-dev
box mAP· 2019-06-24
42.8
best: 66 (Co-DETR)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
16konCOCO test-dev
AP50· 2019-06-24
62.1
best: 82.1 (Plain-DETR (Swin-L))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
16konCOCO test-dev
AP75· 2019-06-24
46.3
best: 71.7 (EVA)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
16konCOCO test-dev
APL· 2019-06-24
55.2
best: 78 (Focal-Stable-DINO (Focal-Huge, no TTA))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
16konCOCO test-dev
APM· 2019-06-24
45.5
best: 67.7 (EVA)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
16konCOCO test-dev
APS· 2019-06-24
23.7
best: 48.6 (Focal-Stable-DINO (Focal-Huge, no TTA))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
16konCOCO test-dev
box mAP· 2019-06-24
42.8
best: 66 (Co-DETR)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756

Computer Vision9 results

Object DetectiononManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
67.6
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
Object DetectiononUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
48.1
best: 52.1 (UniverseNet-20.08)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
Object DetectiononWaymo 2D detection all_ns f0val
COCO-style AP· uses extra data· 2021-03-25
36.4
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
Object DetectiononCOCO test-dev
AP50· 2019-06-24
62.1
best: 82.1 (Plain-DETR (Swin-L))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
Object DetectiononCOCO test-dev
AP75· 2019-06-24
46.3
best: 71.7 (EVA)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
Object DetectiononCOCO test-dev
APL· 2019-06-24
55.2
best: 78 (Focal-Stable-DINO (Focal-Huge, no TTA))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
Object DetectiononCOCO test-dev
APM· 2019-06-24
45.5
best: 67.7 (EVA)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
Object DetectiononCOCO test-dev
APS· 2019-06-24
23.7
best: 48.6 (Focal-Stable-DINO (Focal-Huge, no TTA))
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756
Object DetectiononCOCO test-dev
box mAP· 2019-06-24
42.8
best: 66 (Co-DETR)
Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756