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Models/Faster RCNN

Faster RCNN

Reported on 61 benchmarks across 6 tasks · 4 papers

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

Methodology44 results

3DonSFCHD
mAP@0.50· 2023-06-03
76.4
best: 79.3 (TOOD+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
3DonSFCHD
mAP@0.5:0.95· 2023-06-03
50.3
best: 53.3 (YOLOv8+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
2D ClassificationonSFCHD
mAP@0.50· 2023-06-03
76.4
best: 79.3 (TOOD+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
2D ClassificationonSFCHD
mAP@0.5:0.95· 2023-06-03
50.3
best: 53.3 (YOLOv8+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
2D Object DetectiononSFCHD
mAP@0.50· 2023-06-03
76.4
best: 79.3 (TOOD+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
2D Object DetectiononSFCHD
mAP@0.5:0.95· 2023-06-03
50.3
best: 53.3 (YOLOv8+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
16konSFCHD
mAP@0.50· 2023-06-03
76.4
best: 79.3 (TOOD+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
16konSFCHD
mAP@0.5:0.95· 2023-06-03
50.3
best: 53.3 (YOLOv8+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
3DonCPPE-5
AP50· 2021-12-15
73.8
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
AP75· 2021-12-15
47.8
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
APL· 2021-12-15
52.5
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
APM· 2021-12-15
34.7
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
APS· 2021-12-15
30
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
box AP· 2021-12-15
44
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
AP50· 2021-12-15
73.8
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
AP75· 2021-12-15
47.8
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
APL· 2021-12-15
52.5
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
APM· 2021-12-15
34.7
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
APS· 2021-12-15
30
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
box AP· 2021-12-15
44
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
AP50· 2021-12-15
73.8
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
AP75· 2021-12-15
47.8
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
APL· 2021-12-15
52.5
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
APM· 2021-12-15
34.7
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
APS· 2021-12-15
30
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
box AP· 2021-12-15
44
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
AP50· 2021-12-15
73.8
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
AP75· 2021-12-15
47.8
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
APL· 2021-12-15
52.5
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
APM· 2021-12-15
34.7
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
APS· 2021-12-15
30
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
box AP· 2021-12-15
44
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonNAO
mAP· 2021-11-07
13.5
best: 15.2 (Mask RCNN R50)
Natural Adversarial Objects arXiv:2111.04204
3DonNAO
mAP w/o OOD· 2021-11-07
22.8
best: 29.6 (EfficientDet-D4)
Natural Adversarial Objects arXiv:2111.04204
3DonNAO
mAR· 2021-11-07
41.4
best: 43.8 (Mask RCNN R50)
Natural Adversarial Objects arXiv:2111.04204
2D ClassificationonNAO
mAP· 2021-11-07
13.5
best: 15.2 (Mask RCNN R50)
Natural Adversarial Objects arXiv:2111.04204
2D ClassificationonNAO
mAP w/o OOD· 2021-11-07
22.8
best: 29.6 (EfficientDet-D4)
Natural Adversarial Objects arXiv:2111.04204
2D ClassificationonNAO
mAR· 2021-11-07
41.4
best: 43.8 (Mask RCNN R50)
Natural Adversarial Objects arXiv:2111.04204
2D Object DetectiononNAO
mAP· 2021-11-07
13.5
best: 15.2 (Mask RCNN R50)
Natural Adversarial Objects arXiv:2111.04204
2D Object DetectiononNAO
mAP w/o OOD· 2021-11-07
22.8
best: 29.6 (EfficientDet-D4)
Natural Adversarial Objects arXiv:2111.04204
2D Object DetectiononNAO
mAR· 2021-11-07
41.4
best: 43.8 (Mask RCNN R50)
Natural Adversarial Objects arXiv:2111.04204
16konNAO
mAP· 2021-11-07
13.5
best: 15.2 (Mask RCNN R50)
Natural Adversarial Objects arXiv:2111.04204
16konNAO
mAP w/o OOD· 2021-11-07
22.8
best: 29.6 (EfficientDet-D4)
Natural Adversarial Objects arXiv:2111.04204
16konNAO
mAR· 2021-11-07
41.4
best: 43.8 (Mask RCNN R50)
Natural Adversarial Objects arXiv:2111.04204

Computer Vision17 results

Object DetectiononSFCHD
mAP@0.50· 2023-06-03
76.4
best: 79.3 (TOOD+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
Object DetectiononSFCHD
mAP@0.5:0.95· 2023-06-03
50.3
best: 53.3 (YOLOv8+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
Object DetectiononCPPE-5
AP50· 2021-12-15
73.8
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
AP75· 2021-12-15
47.8
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
APL· 2021-12-15
52.5
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
APM· 2021-12-15
34.7
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
APS· 2021-12-15
30
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
box AP· 2021-12-15
44
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononNAO
mAP· 2021-11-07
13.5
best: 15.2 (Mask RCNN R50)
Natural Adversarial Objects arXiv:2111.04204
Object DetectiononNAO
mAP w/o OOD· 2021-11-07
22.8
best: 29.6 (EfficientDet-D4)
Natural Adversarial Objects arXiv:2111.04204
Object DetectiononNAO
mAR· 2021-11-07
41.4
best: 43.8 (Mask RCNN R50)
Natural Adversarial Objects arXiv:2111.04204
Document Layout AnalysisonPubLayNet val
Figure· 2019-08-16
0.937
best: 0.975 (DETR)
PubLayNet: largest dataset ever for document layout analysis arXiv:1908.07836
Document Layout AnalysisonPubLayNet val
List· 2019-08-16
0.883
best: 0.975 (TRDLU)
PubLayNet: largest dataset ever for document layout analysis arXiv:1908.07836
Document Layout AnalysisonPubLayNet val
Overall· 2019-08-16
0.902
best: 0.962 (VGT)
PubLayNet: largest dataset ever for document layout analysis arXiv:1908.07836
Document Layout AnalysisonPubLayNet val
Table· 2019-08-16
0.954
best: 0.981 (VGT)
PubLayNet: largest dataset ever for document layout analysis arXiv:1908.07836
Document Layout AnalysisonPubLayNet val
Text· 2019-08-16
0.91
best: 0.967 (VSR)
PubLayNet: largest dataset ever for document layout analysis arXiv:1908.07836
Document Layout AnalysisonPubLayNet val
Title· 2019-08-16
0.826
best: 0.939 (VGT)
PubLayNet: largest dataset ever for document layout analysis arXiv:1908.07836