license: mit
https://github.com/baaivision/EVA/tree/master/EVA-CLIP
Summary of EVA-CLIP performance
Model Card
EVA-01-CLIP Series (MIM teacher: OpenAI CLIP-Large)
| 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 | 41K | 256 A100(40GB) | 78.5 | 68.5 | 🤗 HF link (2.2GB) |
EVA01_CLIP_g_14_plus_psz14_s11B |
1.3B | fp16 |
Merged-2B | 114K | 112 A100(40GB) | 79.3 | 74.0 | 🤗 HF link (2.7GB) |
EVA-02-CLIP Series (MIM teacher: EVA01_CLIP_g_14_psz14_s11B)
| model name | image enc. init. ckpt | text enc. init. ckpt | 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 |
EVA02_B_psz14to16 |
openai/clip-vit-base-patch16 |
149M | fp16 |
Merged-2B | 131K | 64 A100(40GB) | 74.7 | 66.9 | 🤗 HF link (300MB) |
EVA02_CLIP_L_psz14_s4B |
EVA02_L_psz14 |
openai/clip-vit-large-patch14 |
428M | fp16 |
Merged-2B | 131K | 128 A100(40GB) | 79.8 | 71.2 | 🤗 HF link (856MB) |
EVA02_CLIP_L_336_psz14_s6B |
EVA02_CLIP_L_psz14_224to336 |
EVA02_CLIP_L_psz14_224to336 |
428M | fp16 |
Merged-2B | 61K | 128 A100(40GB) | 80.4 | 71.7 | 🤗 HF link (856MB) |
EVA02_CLIP_E_psz14_s4B.pt |
EVA02_E_psz14 |
laion/CLIP-ViT-H-14-laion2B-s32B-b79K |
4.7B | fp16 |
LAION-2B | 115K | 144 A100(80GB) | 81.9 | 74.7 | 🤗 HF link (9.4GB) |
EVA02_CLIP_E_psz14_plus_s9B.pt |
EVA02_E_psz14 |
laion/CLIP-ViT-bigG-14-laion2B-39B-b160k |
5.0B | bf16 |
LAION-2B | 144K | 144 A100(80GB) | 82.0 | 75.0 | 🤗 HF link (10.1GB) |
To construct Merged-2B, we merged 1.6 billion samples from LAION-2B dataset with 0.4 billion samples from COYO-700M.
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).
Pretrained
| model name | total #params | training precision | download link |
|---|---|---|---|
EVA01_g_psz14 |
1.0B | fp16 |
🤗 HF link (2.0GB) |
EVA02_B_psz14to16 |
86M | fp16 |
🤗 HF link (176MB) |
EVA02_L_psz14 |
304M | fp16 |
🤗 HF link (609MB) |
EVA02_CLIP_L_psz14_224to336 |
428M | fp16 |
🤗 HF link (857MB) |
EVA02_E_psz14 |
4.4B | fp16 |
🤗 HF link (8.7GB) |
openai/clip-vit-base-patch16 |
149M | fp16 |
🤗 HF link (599MB) |
openai/clip-vit-large-patch14 |
428M | fp16 |
🤗 HF link (1.7GB) |
laion/CLIP-ViT-H-14-laion2B-s32B-b79K |
1.0B | bf16 |
🤗 HF link (3.9GB) |
laion/CLIP-ViT-bigG-14-laion2B-39B-b160k |
1.8B | bf16 |
🤗 HF link part1 part2(9.9GB+169M) |
EVA02_B_psz14to16 interpolates the kernel size of patch_embed from 14x14 to 16x16, and interpolate the pos_embed from 16x16 to 14x14.
EVA02_CLIP_L_psz14_224to336 interpolates the pos_embed from 16x16 to 24x24 for training EVA02_CLIP_L_336_psz14_s6B.
laion/CLIP-ViT-bigG-14-laion2B-39B-b160k consists of 2 parts of weights, part1 and part2.