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Models/RF-ConvNeXt-T Cascade R-CNN

RF-ConvNeXt-T Cascade R-CNN

Reported on 6 benchmarks across 6 tasks · 1 paper · 6 SOTA

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

Methodology4 results

3DonCOCO 2017 val
AP· 2022-06-14
50.9
best: 72.2 (LOGO-CAP (Ours) HRNet-W48)
SOTA
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks arXiv:2206.06637
2D ClassificationonCOCO 2017 val
AP· 2022-06-14
50.9
best: 61.8 (Mr. DETR (Swin-L, 1x, 5cale))
SOTA
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks arXiv:2206.06637
2D Object DetectiononCOCO 2017 val
AP· 2022-06-14
50.9
best: 61.8 (Mr. DETR (Swin-L, 1x, 5cale))
SOTA
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks arXiv:2206.06637
16konCOCO 2017 val
AP· 2022-06-14
50.9
best: 61.8 (Mr. DETR (Swin-L, 1x, 5cale))
SOTA
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks arXiv:2206.06637

Computer Vision2 results

Object DetectiononCOCO 2017 val
AP· 2022-06-14
50.9
best: 61.8 (Mr. DETR (Swin-L, 1x, 5cale))
SOTA
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks arXiv:2206.06637
Instance SegmentationonCOCO 2017 val
AP· 2022-06-14
44.3
best: 45.1 (SparK (ConvNeXt V1-B Mask R-CNN))
SOTA
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks arXiv:2206.06637