add model cards

README.md

@@ -1,4 +1,32 @@
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 license: mit
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-https://github.com/baaivision/EVA/tree/master/EVA-CLIP
+https://github.com/baaivision/EVA/tree/master/EVA-CLIP
+
+## Model Card
+
+### EVA-01-CLIP Series (MIM teacher: [OpenAI CLIP-Large](https://github.com/openai/CLIP))
+<div align="center">
+
+| model name | total #params | training precision | training data  |  training batch size |  gpus for training | IN-1K zero-shot top-1 | MSCOCO T2I R@5 | weight |
+|:-----------|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
+| `EVA01_CLIP_g_14_psz14_s11B` | 1.1B | `fp16` | [LAION-400M](https://laion.ai/blog/laion-400-open-dataset/) | 41K | 256 A100(40GB) | 78.5 | 68.5 | [🤗 HF link](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA01_CLIP_g_14_psz14_s11B.pt) (`2.2GB`) |
+| `EVA01_CLIP_g_14_plus_psz14_s11B` | 1.3B | `fp16` | Merged-2B | 114K | 112 A100(40GB) | 79.3 | 74.0 | [🤗 HF link](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA01_CLIP_g_14_plus_psz14_s11B.pt) (`2.7GB`) |
+</div>
+
+### EVA-02-CLIP Series (MIM teacher: ``EVA01_CLIP_g_14_psz14_s11B``)
+<div align="center">
+
+| model name | total #params | training precision | training data  |  training batch size |  gpus for training | IN-1K zero-shot top-1 | MSCOCO T2I R@5 | weight |
+|:-----------|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
+| `EVA02_CLIP_B_psz16_s8B` | 149M | `fp16` | Merged-2B | 131K | 64 A100(40GB) | 74.7 | 66.9 | [🤗 HF link](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_B_psz16_s8B.pt) (`300MB`) |
+| `EVA02_CLIP_L_psz14_s4B` | 428M | `fp16` | Merged-2B | 131K | 128 A100(40GB) | 79.8 | 71.2 | [🤗 HF link](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_L_psz14_s4B.pt) (`856MB`) |
+| `EVA02_CLIP_L_336_psz14_s6B` | 428M | `fp16` | Merged-2B | 61K | 128 A100(40GB) | 80.4 | 71.7 | [🤗 HF link](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_L_336_psz14_s6B.pt) (`856MB`) |
+| `EVA02_CLIP_E_psz14_s4B.pt` | 4.7B | `fp16` | [LAION-2B](https://laion.ai/blog/laion-5b/) | 144K | 144 A100(80GB) | 81.9 | 74.7 | [🤗 HF link](https://huggingface.co/QuanSun/EVA-CLIP/blob/main/EVA02_CLIP_E_psz14_s4B.pt) (`9.4GB`) |
+
+</div>
+
+- To construct Merged-2B, we merged 1.6 billion samples from [LAION-2B](https://laion.ai/blog/laion-5b/) dataset with 0.4 billion samples from [COYO-700M](https://github.com/kakaobrain/coyo-dataset).
+
+- To our knowledge, EVA-CLIP series are the most performant open-sourced CLIP models at all scales, evaluated via zero-shot classification performance, especially on mainstream classification benchmarks such as ImageNet along with its variants.
+For more details about EVA-CLIP, please refer to our [paper (coming very soon)]().