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Models/Prova (Swin-Base)

Prova (Swin-Base)

Reported on 6 benchmarks across 6 tasks · 1 paper

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

Methodology4 results

3DonLVIS v1.0
AP novel-LVIS base training· uses extra data· 2024-12-23
31.5
best: 43.4 (LaMI-DETR)
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection arXiv:2412.17800
2D ClassificationonLVIS v1.0
AP novel-LVIS base training· uses extra data· 2024-12-23
31.5
best: 43.4 (LaMI-DETR)
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection arXiv:2412.17800
2D Object DetectiononLVIS v1.0
AP novel-LVIS base training· uses extra data· 2024-12-23
31.5
best: 43.4 (LaMI-DETR)
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection arXiv:2412.17800
16konLVIS v1.0
AP novel-LVIS base training· uses extra data· 2024-12-23
31.5
best: 43.4 (LaMI-DETR)
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection arXiv:2412.17800

Computer Vision2 results

Object DetectiononLVIS v1.0
AP novel-LVIS base training· uses extra data· 2024-12-23
31.5
best: 43.4 (LaMI-DETR)
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection arXiv:2412.17800
Open Vocabulary Object DetectiononLVIS v1.0
AP novel-LVIS base training· uses extra data· 2024-12-23
31.5
best: 43.4 (LaMI-DETR)
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection arXiv:2412.17800