---
tags:
- clip
library_name: open_clip
pipeline_tag: zero-shot-image-classification
license: cc-by-nc-4.0
datasets:
- visheratin/laion-coco-nllb
---

## Model Summary

NLLB-SigLIP-MRL is a model that combines a text encoder from the [NLLB model](https://huggingface.co/facebook/nllb-200-distilled-600M) and an image encoder from the 
[SigLIP](https://huggingface.co/timm/ViT-B-16-SigLIP-384) model. This allows us to extend the model capabilities 
to 201 languages of the Flores-200. This version of the model was trained using a variation of [Matryoshka Representation learning](https://arxiv.org/abs/2205.13147) 
to enable the generation of embeddings of sizes [32, 64, 128, 256, 512] in addition to the original 768. Based on the benchmarks below, embeddings of sizes 256 and 512 
preserve 90%+ of the full embedding quality.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/609ede05121df5de54007033/PP5GJOgM2YVQM4RSWHKtq.png)

The full embedding model sets new state-of-the-art for multilingual image and text retrieval on both XTD10 and Crossmodal-3600.

| Dataset         | image retrieval R@1, avg | text retrieval R@1, avg | image retrieval R@5, avg | text retrieval R@5, avg | image retrieval R@10, avg | text retrieval R@10, avg |
|-----------------|:---------------------:|:--------------------:|:---------------------:|:--------------------:|:----------------------:|:---------------------:|
| Crossmodal-3600 |        0.5539       |             0.5232 |     0.7963          |       0.7792       |        0.8643        |      0.8558         |
| XTD10           |        0.6559       |             0.6106 |     0.8846          |       0.8643       |        0.9458        |      0.9379         |

## How to use

### Variable resolutions

<a target="_blank" href="https://colab.research.google.com/drive/1gYKUm3urhhHapaFbJ6GD1Fl3pI5g-fjM">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

If you want to use the model that supports variable embedding sizes, you can do it as follows:

```
!pip install -U transformers open_clip_torch
```

```
from transformers import AutoModel
from PIL import Image
import requests
import torch

model = AutoModel.from_pretrained("visheratin/nllb-siglip-mrl-base", device="cpu", trust_remote_code=True)

image_path = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
image = Image.open(requests.get(image_path, stream=True).raw)

class_options = ["бабочка", "butterfly", "kat"]
class_langs = ["rus_Cyrl", "eng_Latn", "afr_Latn"]

image_logits, text_logits = model.get_logits(
    images=[image],
    texts=class_options,
    langs=class_langs,
    resolution=512 # set resolution here or set `None` to use the original resolution
)

print(torch.softmax(image_logits, dim=1))
```

### OpenCLIP

This model is also integrated into OpenCLIP so that you can use it as any other model:

```
!pip install -U open_clip_torch
```

```
from open_clip import create_model_from_pretrained, get_tokenizer
from PIL import Image
import requests
import torch

model, transform = create_model_from_pretrained("nllb-clip-base-siglip", "mrl", device="cuda")

tokenizer = get_tokenizer("nllb-clip-base-siglip")

class_options = ["бабочка", "butterfly", "kat"]
class_langs = ["rus_Cyrl", "eng_Latn", "afr_Latn"]

text_inputs = []
for i in range(len(class_options)):
    tokenizer.set_language(class_langs[i])
    text_inputs.append(tokenizer(class_options[i]))
text_inputs = torch.stack(text_inputs).squeeze(1).to("cuda")

image_path = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
image = Image.open(requests.get(image_path, stream=True).raw)

image_inputs = transform(image).unsqueeze(0).to("cuda")

with torch.inference_mode():
    logits_per_image, logits_per_text = model.get_logits(image_inputs, text_inputs)

print(logits_per_image.softmax(dim=-1))
```

## Acknowledgements

I thank [ML Collective](https://mlcollective.org/) for providing Google Cloud compute resources.