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Models/NAS-FPN (AmoebaNet-D, learned aug)

NAS-FPN (AmoebaNet-D, learned aug)

Reported on 20 benchmarks across 5 tasks · 1 paper · 18 SOTA

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

Methodology16 results

3DonCOCO test-dev
APS· 2019-06-26
34.2
best: 48.6 (Focal-Stable-DINO (Focal-Huge, no TTA))
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
3DonCOCO test-dev
box mAP· 2019-06-26
50.7
best: 66 (Co-DETR)
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
2D ClassificationonCOCO test-dev
APL· 2019-06-26
64.5
best: 78 (Focal-Stable-DINO (Focal-Huge, no TTA))
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
2D ClassificationonCOCO test-dev
APM· 2019-06-26
55.5
best: 67.7 (EVA)
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
2D ClassificationonCOCO test-dev
APS· 2019-06-26
34.2
best: 48.6 (Focal-Stable-DINO (Focal-Huge, no TTA))
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
2D ClassificationonCOCO test-dev
box mAP· 2019-06-26
50.7
best: 66 (Co-DETR)
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
2D Object DetectiononCOCO test-dev
APL· 2019-06-26
64.5
best: 78 (Focal-Stable-DINO (Focal-Huge, no TTA))
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
2D Object DetectiononCOCO test-dev
APM· 2019-06-26
55.5
best: 67.7 (EVA)
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
2D Object DetectiononCOCO test-dev
APS· 2019-06-26
34.2
best: 48.6 (Focal-Stable-DINO (Focal-Huge, no TTA))
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
2D Object DetectiononCOCO test-dev
box mAP· 2019-06-26
50.7
best: 66 (Co-DETR)
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
16konCOCO test-dev
APL· 2019-06-26
64.5
best: 78 (Focal-Stable-DINO (Focal-Huge, no TTA))
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
16konCOCO test-dev
APM· 2019-06-26
55.5
best: 67.7 (EVA)
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
16konCOCO test-dev
APS· 2019-06-26
34.2
best: 48.6 (Focal-Stable-DINO (Focal-Huge, no TTA))
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
16konCOCO test-dev
box mAP· 2019-06-26
50.7
best: 66 (Co-DETR)
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
3DonCOCO test-dev
APL· 2019-06-26
64.5
best: 86.5 (PoseBH-H)
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
3DonCOCO test-dev
APM· 2019-06-26
55.5
best: 83.8 (4xRSN-50 (ensemble))
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172

Computer Vision4 results

Object DetectiononCOCO test-dev
APL· 2019-06-26
64.5
best: 78 (Focal-Stable-DINO (Focal-Huge, no TTA))
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
Object DetectiononCOCO test-dev
APM· 2019-06-26
55.5
best: 67.7 (EVA)
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
Object DetectiononCOCO test-dev
APS· 2019-06-26
34.2
best: 48.6 (Focal-Stable-DINO (Focal-Huge, no TTA))
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172
Object DetectiononCOCO test-dev
box mAP· 2019-06-26
50.7
best: 66 (Co-DETR)
SOTA
Learning Data Augmentation Strategies for Object Detection arXiv:1906.11172