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/FCOS

FCOS

Reported on 66 benchmarks across 7 tasks · 4 papers · 8 SOTA

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

Methodology37 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
49.6
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
49.6
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
49.6
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
49.6
best: 53.3 (YOLOv8+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
3DonDeepLesion
Sensitivity· 2022-03-30
86.05
best: 88.55 (P3D)
An Efficient Anchor-free Universal Lesion Detection in CT-scans arXiv:2203.16074
2D ClassificationonDeepLesion
Sensitivity· 2022-03-30
86.05
best: 88.55 (P3D)
An Efficient Anchor-free Universal Lesion Detection in CT-scans arXiv:2203.16074
2D Object DetectiononDeepLesion
Sensitivity· 2022-03-30
86.05
best: 88.55 (P3D)
An Efficient Anchor-free Universal Lesion Detection in CT-scans arXiv:2203.16074
16konDeepLesion
Sensitivity· 2022-03-30
86.05
best: 88.55 (P3D)
An Efficient Anchor-free Universal Lesion Detection in CT-scans arXiv:2203.16074
3DonCPPE-5
AP50· 2021-12-15
79.5
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
AP75· 2021-12-15
45.9
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
APL· 2021-12-15
51.7
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
APM· 2021-12-15
39.2
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
APS· 2021-12-15
36.7
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
box AP· 2021-12-15
44.4
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
AP50· 2021-12-15
79.5
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
AP75· 2021-12-15
45.9
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
APL· 2021-12-15
51.7
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
APM· 2021-12-15
39.2
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
APS· 2021-12-15
36.7
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
box AP· 2021-12-15
44.4
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
AP50· 2021-12-15
79.5
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
AP75· 2021-12-15
45.9
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
APL· 2021-12-15
51.7
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
APM· 2021-12-15
39.2
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
APS· 2021-12-15
36.7
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
box AP· 2021-12-15
44.4
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
AP50· 2021-12-15
79.5
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
AP75· 2021-12-15
45.9
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
APL· 2021-12-15
51.7
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
APM· 2021-12-15
39.2
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
APS· 2021-12-15
36.7
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
box AP· 2021-12-15
44.4
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononSARDet-100K
box mAP· 2019-04-02
49.8
best: 55.4 (DenoDet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355

Computer Vision19 results

Pedestrian DetectiononTJU-Ped-traffic
HO (miss rate)· 2019-04-02
63.73
SOTA
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Pedestrian DetectiononTJU-Ped-traffic
R (miss rate)· 2019-04-02
24.35
SOTA
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Pedestrian DetectiononTJU-Ped-traffic
R+HO (miss rate)· 2019-04-02
28.86
SOTA
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Pedestrian DetectiononTJU-Ped-campus
HO (miss rate)· 2019-04-02
81.28
SOTA
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
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
49.6
best: 53.3 (YOLOv8+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
Object DetectiononDeepLesion
Sensitivity· 2022-03-30
86.05
best: 88.55 (P3D)
An Efficient Anchor-free Universal Lesion Detection in CT-scans arXiv:2203.16074
Object DetectiononCPPE-5
AP50· 2021-12-15
79.5
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
AP75· 2021-12-15
45.9
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
APL· 2021-12-15
51.7
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
APM· 2021-12-15
39.2
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
APS· 2021-12-15
36.7
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
box AP· 2021-12-15
44.4
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Pedestrian DetectiononTJU-Ped-traffic
ALL (miss rate)· 2019-04-02
40.02
best: 41.4 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Pedestrian DetectiononTJU-Ped-traffic
RS (miss rate)· 2019-04-02
37.4
best: 37.92 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Pedestrian DetectiononTJU-Ped-campus
ALL (miss rate)· 2019-04-02
41.62
best: 44.34 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Pedestrian DetectiononTJU-Ped-campus
R (miss rate)· 2019-04-02
31.89
best: 34.73 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Pedestrian DetectiononTJU-Ped-campus
R+HO (miss rate)· 2019-04-02
39.38
best: 42.26 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Pedestrian DetectiononTJU-Ped-campus
RS (miss rate)· 2019-04-02
69.04
best: 82.99 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355

Robots10 results

Autonomous VehiclesonTJU-Ped-traffic
HO (miss rate)· 2019-04-02
63.73
SOTA
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Autonomous VehiclesonTJU-Ped-traffic
R (miss rate)· 2019-04-02
24.35
SOTA
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Autonomous VehiclesonTJU-Ped-traffic
R+HO (miss rate)· 2019-04-02
28.86
SOTA
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Autonomous VehiclesonTJU-Ped-campus
HO (miss rate)· 2019-04-02
81.28
SOTA
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Autonomous VehiclesonTJU-Ped-traffic
ALL (miss rate)· 2019-04-02
40.02
best: 41.4 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Autonomous VehiclesonTJU-Ped-traffic
RS (miss rate)· 2019-04-02
37.4
best: 37.92 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Autonomous VehiclesonTJU-Ped-campus
ALL (miss rate)· 2019-04-02
41.62
best: 44.34 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Autonomous VehiclesonTJU-Ped-campus
R (miss rate)· 2019-04-02
31.89
best: 34.73 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Autonomous VehiclesonTJU-Ped-campus
R+HO (miss rate)· 2019-04-02
39.38
best: 42.26 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355
Autonomous VehiclesonTJU-Ped-campus
RS (miss rate)· 2019-04-02
69.04
best: 82.99 (RetinaNet)
FCOS: Fully Convolutional One-Stage Object Detection arXiv:1904.01355