Zero Shot Transfer Image Classification On 6
评估指标
Accuracy (Private)
评测结果
各个模型在此基准测试上的表现结果
模型名称 | Accuracy (Private) | Paper Title | Repository |
|---|---|---|---|
| InternVL-C | 80.6 | InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks | - |
| LiT-tuning | 81.1 | LiT: Zero-Shot Transfer with Locked-image text Tuning | - |
| PaLI | 42.62 | PaLI: A Jointly-Scaled Multilingual Language-Image Model | - |
| EVA-CLIP-E/14+ | 79.6 | EVA-CLIP: Improved Training Techniques for CLIP at Scale | - |
| LiT-22B | 87.6 | Scaling Vision Transformers to 22 Billion Parameters | - |
| EVA-CLIP-18B | 82.2 | EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters | - |
| CLIP | 72.3 | Learning Transferable Visual Models From Natural Language Supervision | - |
| LiT ViT-e | 84.9 | PaLI: A Jointly-Scaled Multilingual Language-Image Model | - |
| CoCa | 82.7 | CoCa: Contrastive Captioners are Image-Text Foundation Models | - |
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