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*This model was contributed to Hugging Face Transformers on 2025-08-20.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
    </div>
</div>

# MetaCLIP 2

## Overview

MetaCLIP 2 is a replication of the original CLIP model trained on 300+ languages. It achieves state-of-the-art (SOTA) results on multilingual benchmarks (e.g., XM3600, CVQA, Babel‑ImageNet), surpassing previous SOTA such as [mSigLIP](siglip) and [SigLIP‑2](siglip2). The authors show that English and non-English worlds can mutually benefit and elevate each other.

This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/facebookresearch/MetaCLIP).

You can find all the MetaCLIP 2 checkpoints under the [Meta](https://huggingface.co/facebook/models?search=metaclip-2) organization.

> [!TIP]
> Click on the MetaCLIP 2 models in the right sidebar for more examples of how to apply MetaCLIP 2 to different image and language tasks.

The example below demonstrates how to calculate similarity scores between multiple text descriptions and an image with [`Pipeline`] or the [`AutoModel`] class. Usage of the MetaCLIP 2 models is identical to the CLIP models, you just need the `MetaClip2Model` class instead of `CLIPModel`.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
import torch

from transformers import pipeline


clip = pipeline(
   task="zero-shot-image-classification",
   model="facebook/metaclip-2-worldwide-huge-quickgelu",
   device=0
)
labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]
clip("http://images.cocodataset.org/val2017/000000039769.jpg", candidate_labels=labels)
```

</hfoption>
<hfoption id="AutoModel">

```python
import requests
from PIL import Image

from transformers import AutoModel, AutoProcessor


model = AutoModel.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu", attn_implementation="sdpa", device_map="auto")
processor = AutoProcessor.from_pretrained("facebook/metaclip-2-worldwide-huge-quickgelu")

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]

inputs = processor(text=labels, images=image, return_tensors="pt", padding=True).to(model.device)

outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)
most_likely_idx = probs.argmax(dim=1).item()
most_likely_label = labels[most_likely_idx]
print(f"Most likely label: {most_likely_label} with probability: {probs[0][most_likely_idx].item():.3f}")
```

</hfoption>
</hfoptions>

## MetaClip2Config

[[autodoc]] MetaClip2Config

## MetaClip2TextConfig

[[autodoc]] MetaClip2TextConfig

## MetaClip2VisionConfig

[[autodoc]] MetaClip2VisionConfig

## MetaClip2Model

[[autodoc]] MetaClip2Model
    - forward
    - get_text_features
    - get_image_features

## MetaClip2TextModel

[[autodoc]] MetaClip2TextModel
    - forward

## MetaClip2TextModelWithProjection

[[autodoc]] MetaClip2TextModelWithProjection
    - forward

## MetaClip2VisionModelWithProjection

[[autodoc]] MetaClip2VisionModelWithProjection
    - forward

## MetaClip2VisionModel

[[autodoc]] MetaClip2VisionModel
    - forward

## MetaClip2ForImageClassification

[[autodoc]] MetaClip2ForImageClassification
    - forward
