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# ruclip-vit-large-patch14-336
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) 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](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) 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](https://github.com/sberbank-ai/ru-clip)
```
pip install ruclip
```
```python
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 |
# Authors
+ Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov)
+ Daniil Chesakov: [Github](https://github.com/Danyache)
+ Denis Dimitrov: [Github](https://github.com/denndimitrov)
+ Igor Pavlov: [Github](https://github.com/boomb0om)
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