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Models/RetinaNet

RetinaNet

Reported on 58 benchmarks across 8 tasks · 4 papers · 32 SOTA

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

Methodology29 results

3DonSKU-110K
AP· 2017-08-07
45.5
best: 59 (RetailDet)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
3DonSKU-110K
AP75· 2017-08-07
0.389
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
2D ClassificationonSKU-110K
AP· 2017-08-07
45.5
best: 59 (RetailDet)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
2D ClassificationonSKU-110K
AP75· 2017-08-07
0.389
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
2D Object DetectiononSKU-110K
AP· 2017-08-07
45.5
best: 59 (RetailDet)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
2D Object DetectiononSKU-110K
AP75· 2017-08-07
0.389
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
16konSKU-110K
AP· 2017-08-07
45.5
best: 59 (RetailDet)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
16konSKU-110K
AP75· 2017-08-07
0.389
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
3DonSFCHD
mAP@0.50· 2023-06-03
75.9
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
48.9
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
75.9
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
48.9
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
75.9
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
48.9
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
75.9
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
48.9
best: 53.3 (YOLOv8+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
3DonManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
65.3
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
3DonUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
44.8
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
32.5
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
65.3
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
44.8
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
32.5
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
65.3
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
44.8
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
32.5
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
65.3
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
16konUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
44.8
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
32.5
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D Object DetectiononSARDet-100K
box mAP· 2017-08-07
47.4
best: 55.4 (DenoDet)
Focal Loss for Dense Object Detection arXiv:1708.02002

Computer Vision17 results

Object DetectiononSKU-110K
AP· 2017-08-07
45.5
best: 59 (RetailDet)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Object DetectiononSKU-110K
AP75· 2017-08-07
0.389
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Pedestrian DetectiononTJU-Ped-traffic
ALL (miss rate)· 2017-08-07
41.4
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Pedestrian DetectiononTJU-Ped-traffic
HO (miss rate)· 2017-08-07
61.6
best: 63.73 (FCOS)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Pedestrian DetectiononTJU-Ped-traffic
R (miss rate)· 2017-08-07
23.89
best: 24.35 (FCOS)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Pedestrian DetectiononTJU-Ped-traffic
R+HO (miss rate)· 2017-08-07
28.45
best: 28.86 (FCOS)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Pedestrian DetectiononTJU-Ped-traffic
RS (miss rate)· 2017-08-07
37.92
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Pedestrian DetectiononTJU-Ped-campus
ALL (miss rate)· 2017-08-07
44.34
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Pedestrian DetectiononTJU-Ped-campus
HO (miss rate)· 2017-08-07
71.31
best: 81.28 (FCOS)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Pedestrian DetectiononTJU-Ped-campus
R (miss rate)· 2017-08-07
34.73
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Pedestrian DetectiononTJU-Ped-campus
R+HO (miss rate)· 2017-08-07
42.26
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Pedestrian DetectiononTJU-Ped-campus
RS (miss rate)· 2017-08-07
82.99
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Object DetectiononSFCHD
mAP@0.50· 2023-06-03
75.9
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
48.9
best: 53.3 (YOLOv8+SCALE)
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method arXiv:2306.02098
Object DetectiononManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
65.3
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
44.8
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
32.5
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027

Robots10 results

Autonomous VehiclesonTJU-Ped-traffic
ALL (miss rate)· 2017-08-07
41.4
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Autonomous VehiclesonTJU-Ped-traffic
HO (miss rate)· 2017-08-07
61.6
best: 63.73 (FCOS)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Autonomous VehiclesonTJU-Ped-traffic
R (miss rate)· 2017-08-07
23.89
best: 24.35 (FCOS)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Autonomous VehiclesonTJU-Ped-traffic
R+HO (miss rate)· 2017-08-07
28.45
best: 28.86 (FCOS)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Autonomous VehiclesonTJU-Ped-traffic
RS (miss rate)· 2017-08-07
37.92
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Autonomous VehiclesonTJU-Ped-campus
ALL (miss rate)· 2017-08-07
44.34
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Autonomous VehiclesonTJU-Ped-campus
HO (miss rate)· 2017-08-07
71.31
best: 81.28 (FCOS)
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Autonomous VehiclesonTJU-Ped-campus
R (miss rate)· 2017-08-07
34.73
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Autonomous VehiclesonTJU-Ped-campus
R+HO (miss rate)· 2017-08-07
42.26
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002
Autonomous VehiclesonTJU-Ped-campus
RS (miss rate)· 2017-08-07
82.99
SOTA
Focal Loss for Dense Object Detection arXiv:1708.02002

Miscellaneous2 results

Table DetectiononSTDW
AP· 2022-08-31
0.78
SOTA
Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method arXiv:2209.09207
Table DetectiononSTDW
IoU· 2022-08-31
0.5
SOTA
Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method arXiv:2209.09207