ruclip-vit-large-patch14-336
RuCLIP (Russian Contrastive Language–Image Pretraining) is a multimodal model for obtaining images and text similarities and rearranging captions and pictures. RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processing and multimodal learning.
Model was trained by Sber AI and SberDevices teams.
- Task:
text ranking;image ranking;zero-shot image classification; - Type:
encoder - Num Parameters:
430M - Training Data Volume:
240 million text-image pairs - Language:
Russian - Context Length:
77 - Transformer Layers:
12 - Transformer Width:
768 - Transformer Heads:
12 - Image Size:
336 - Vision Layers:
24 - Vision Width:
1024 - Vision Patch Size:
14
Usage Github
pip install ruclip
clip, processor = ruclip.load("ruclip-vit-large-patch14-336", device="cuda")
Performance
We have evaluated the performance on the following datasets:
| Dataset | Metric Name | Metric Result |
|---|---|---|
| Food101 | acc | 0.712 |
| CIFAR10 | acc | 0.906 |
| CIFAR100 | acc | 0.591 |
| Birdsnap | acc | 0.213 |
| SUN397 | acc | 0.523 |
| Stanford Cars | acc | 0.659 |
| DTD | acc | 0.408 |
| MNIST | acc | 0.242 |
| STL10 | acc | 0.956 |
| PCam | acc | 0.554 |
| CLEVR | acc | 0.142 |
| Rendered SST2 | acc | 0.539 |
| ImageNet | acc | 0.488 |
| FGVC Aircraft | mean-per-class | 0.075 |
| Oxford Pets | mean-per-class | 0.546 |
| Caltech101 | mean-per-class | 0.835 |
| Flowers102 | mean-per-class | 0.517 |
| HatefulMemes | roc-auc | 0.519 |