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Models/MaskConver (ResNet50, single-scale)

MaskConver (ResNet50, single-scale)

Reported on 9 benchmarks across 3 tasks · 1 paper · 3 SOTA

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

Medical3 results

Semantic SegmentationonCOCO test-dev
PQst· 2023-12-11
58.9
SOTA
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation arXiv:2312.06052
Semantic SegmentationonCOCO test-dev
PQ· 2023-12-11
53.6
best: 59.5 (Mask DINO (single scale))
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation arXiv:2312.06052
Semantic SegmentationonCOCO test-dev
PQth· 2023-12-11
45.6
best: 65.1 (Mask2Former (Swin-L))
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation arXiv:2312.06052

Audio3 results

10-shot image generationonCOCO test-dev
PQst· 2023-12-11
58.9
SOTA
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation arXiv:2312.06052
10-shot image generationonCOCO test-dev
PQ· 2023-12-11
53.6
best: 59.5 (Mask DINO (single scale))
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation arXiv:2312.06052
10-shot image generationonCOCO test-dev
PQth· 2023-12-11
45.6
best: 65.1 (Mask2Former (Swin-L))
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation arXiv:2312.06052

Computer Vision3 results

Panoptic SegmentationonCOCO test-dev
PQst· 2023-12-11
58.9
SOTA
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation arXiv:2312.06052
Panoptic SegmentationonCOCO test-dev
PQ· 2023-12-11
53.6
best: 59.5 (Mask DINO (single scale))
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation arXiv:2312.06052
Panoptic SegmentationonCOCO test-dev
PQth· 2023-12-11
45.6
best: 65.1 (Mask2Former (Swin-L))
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation arXiv:2312.06052