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Models/SSD

SSD

Reported on 65 benchmarks across 14 tasks · 8 papers · 10 SOTA

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

Methodology46 results

Anomaly DetectiononAnomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix)
ROC-AUC· 2021-03-22
96.5
best: 99.1 (PsudoLabels ViT)
SOTA
SSD: A Unified Framework for Self-Supervised Outlier Detection arXiv:2103.12051
Anomaly DetectiononUnlabeled CIFAR-10 vs CIFAR-100
AUROC· 2021-03-22
89.6
best: 96.7 (PsudoLabels ViT)
SOTA
SSD: A Unified Framework for Self-Supervised Outlier Detection arXiv:2103.12051
3DonPKU-DDD17-Car
mAP50· 2015-12-08
73.1
best: 86.7 (CAFR)
SOTA
SSD: Single Shot MultiBox Detector arXiv:1512.02325
2D ClassificationonPKU-DDD17-Car
mAP50· 2015-12-08
73.1
best: 86.7 (CAFR)
SOTA
SSD: Single Shot MultiBox Detector arXiv:1512.02325
2D Object DetectiononPKU-DDD17-Car
mAP50· 2015-12-08
73.1
best: 86.7 (CAFR)
SOTA
SSD: Single Shot MultiBox Detector arXiv:1512.02325
16konPKU-DDD17-Car
mAP50· 2015-12-08
73.1
best: 86.7 (CAFR)
SOTA
SSD: Single Shot MultiBox Detector arXiv:1512.02325
3DonSFCHD
mAP@0.50· 2023-06-03
72.8
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
41.5
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
72.8
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
41.5
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
72.8
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
41.5
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
72.8
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
41.5
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
57
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
AP75· 2021-12-15
24.9
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
APL· 2021-12-15
34.6
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
APM· 2021-12-15
23.1
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
APS· 2021-12-15
32.1
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
3DonCPPE-5
box AP· 2021-12-15
29.5
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
AP50· 2021-12-15
57
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
AP75· 2021-12-15
24.9
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
APL· 2021-12-15
34.6
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
APM· 2021-12-15
23.1
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
APS· 2021-12-15
32.1
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D ClassificationonCPPE-5
box AP· 2021-12-15
29.5
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
AP50· 2021-12-15
57
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
AP75· 2021-12-15
24.9
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
APL· 2021-12-15
34.6
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
APM· 2021-12-15
23.1
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
APS· 2021-12-15
32.1
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
2D Object DetectiononCPPE-5
box AP· 2021-12-15
29.5
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
AP50· 2021-12-15
57
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
AP75· 2021-12-15
24.9
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
APL· 2021-12-15
34.6
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
APM· 2021-12-15
23.1
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
APS· 2021-12-15
32.1
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
16konCPPE-5
box AP· 2021-12-15
29.5
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Generalized Few-Shot ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2021-09-09
54
best: 10.9 (LPT)
Self Supervision to Distillation for Long-Tailed Visual Recognition arXiv:2109.04075
Long-tail LearningonCIFAR-100-LT (ρ=100)
Error Rate· 2021-09-09
54
best: 10.9 (LPT)
Self Supervision to Distillation for Long-Tailed Visual Recognition arXiv:2109.04075
Generalized Few-Shot LearningonCIFAR-100-LT (ρ=100)
Error Rate· 2021-09-09
54
best: 10.9 (LPT)
Self Supervision to Distillation for Long-Tailed Visual Recognition arXiv:2109.04075
Anomaly DetectiononOne-class CIFAR-10
AUROC· 2021-03-22
90
best: 99.6 (CLIP (OE))
SSD: A Unified Framework for Self-Supervised Outlier Detection arXiv:2103.12051
3DonUAVDT
mAP· 2018-03-26
33.62
best: 76.55 (PRB-FPN)
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking arXiv:1804.00518
2D ClassificationonUAVDT
mAP· 2018-03-26
33.62
best: 76.55 (PRB-FPN)
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking arXiv:1804.00518
2D Object DetectiononUAVDT
mAP· 2018-03-26
33.62
best: 76.55 (PRB-FPN)
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking arXiv:1804.00518
16konUAVDT
mAP· 2018-03-26
33.62
best: 76.55 (PRB-FPN)
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking arXiv:1804.00518

Computer Vision19 results

Out-of-Distribution DetectiononImageNet-1k vs Textures
AUROC· 2021-03-22
85.4
best: 98.92 (ViM (BiT))
SOTA
SSD: A Unified Framework for Self-Supervised Outlier Detection arXiv:2103.12051
Out-of-Distribution DetectiononImageNet-1k vs Textures
FPR95· 2021-03-22
57.2
best: 79.8 (RP+GradNorm)
SOTA
SSD: A Unified Framework for Self-Supervised Outlier Detection arXiv:2103.12051
Person RetrievalonSoftBioSearch
Average IOU· 2018-09-24
0.503
SOTA
Person Retrieval in Surveillance Video using Height, Color and Gender arXiv:1810.05080
Object DetectiononPKU-DDD17-Car
mAP50· 2015-12-08
73.1
best: 86.7 (CAFR)
SOTA
SSD: Single Shot MultiBox Detector arXiv:1512.02325
Object CountingonFSC147
MAE(test)· 2024-05-20
9.58
best: 5.74 (CountGD)
Learning Spatial Similarity Distribution for Few-shot Object Counting arXiv:2405.11770
Object CountingonFSC147
MAE(val)· 2024-05-20
9.73
best: 7.1 (CountGD)
Learning Spatial Similarity Distribution for Few-shot Object Counting arXiv:2405.11770
Object CountingonFSC147
RMSE(test)· 2024-05-20
64.13
best: 24.09 (CountGD)
Learning Spatial Similarity Distribution for Few-shot Object Counting arXiv:2405.11770
Object CountingonFSC147
RMSE(val)· 2024-05-20
29.72
best: 26.08 (CountGD)
Learning Spatial Similarity Distribution for Few-shot Object Counting arXiv:2405.11770
Object DetectiononSFCHD
mAP@0.50· 2023-06-03
72.8
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
41.5
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
57
best: 87.3 (Double Heads)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
AP75· 2021-12-15
24.9
best: 58.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
APL· 2021-12-15
34.6
best: 62.6 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
APM· 2021-12-15
23.1
best: 43.4 (Empirical Attention)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
APS· 2021-12-15
32.1
best: 45.8 (Localization Distillation)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Object DetectiononCPPE-5
box AP· 2021-12-15
29.5
best: 52.9 (TridentNet)
CPPE-5: Medical Personal Protective Equipment Dataset arXiv:2112.09569
Image ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2021-09-09
54
best: 10.9 (LPT)
Self Supervision to Distillation for Long-Tailed Visual Recognition arXiv:2109.04075
Few-Shot Image ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2021-09-09
54
best: 10.9 (LPT)
Self Supervision to Distillation for Long-Tailed Visual Recognition arXiv:2109.04075
Object DetectiononUAVDT
mAP· 2018-03-26
33.62
best: 76.55 (PRB-FPN)
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking arXiv:1804.00518