---
library_name: coreml
license: other
license_name: apple-ascl
license_link: https://github.com/apple/ml-mobileclip/blob/main/LICENSE_weights_data
datasets:
- apple/DataCompDR-1B
---

# MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training

MobileCLIP was introduced in [MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
](https://arxiv.org/pdf/2311.17049.pdf) (CVPR 2024), by Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel.

This repository contains the text and image encoders of all variants of MobileCLIP exported to Core ML. These Core ML models can be plugged-into the demo app provided in the official [MobileCLIP repo](https://github.com/apple/ml-mobileclip)

<img src="mce_example.gif" width="240" height="540" />

### Highlights

* Our smallest variant `MobileCLIP-S0` obtains similar zero-shot performance as [OpenAI](https://arxiv.org/abs/2103.00020)'s ViT-B/16 model while being 4.8x faster and 2.8x smaller.
* `MobileCLIP-S2` obtains better avg zero-shot performance than [SigLIP](https://arxiv.org/abs/2303.15343)'s ViT-B/16 model while being 2.3x faster and 2.1x smaller, and trained with 3x less seen samples.
* `MobileCLIP-B`(LT) attains zero-shot ImageNet performance of **77.2%** which is significantly better than recent works like [DFN](https://arxiv.org/abs/2309.17425) and [SigLIP](https://arxiv.org/abs/2303.15343) with similar architectures or even [OpenAI's ViT-L/14@336](https://arxiv.org/abs/2103.00020).

## Checkpoints

| Model                                                     | # Seen <BR>Samples (B) | # Params (M) <BR> (img + txt) | Latency (ms) <BR> (img + txt) | IN-1k Zero-Shot <BR> Top-1 Acc. (%) | Avg. Perf. (%) <BR> on 38 datasets |
|:----------------------------------------------------------|:----------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------------:|:----------------------------------:|
| [MobileCLIP-S0](https://hf.co/pcuenq/MobileCLIP-S0)       |           13           |          11.4 + 42.4          |           1.5 + 1.6           |                67.8                 |                58.1                |
| [MobileCLIP-S1](https://hf.co/pcuenq/MobileCLIP-S1)       |           13           |          21.5 + 63.4          |           2.5 + 3.3           |                72.6                 |                61.3                |
| [MobileCLIP-S2](https://hf.co/pcuenq/MobileCLIP-S2)       |           13           |          35.7 + 63.4          |           3.6 + 3.3           |                74.4                 |                63.7                |
| [MobileCLIP-B](https://hf.co/pcuenq/MobileCLIP-B)         |           13           |          86.3 + 63.4          |          10.4 + 3.3           |                76.8                 |                65.2                |
| [MobileCLIP-B (LT)](https://hf.co/pcuenq/MobileCLIP-B-LT) |           36           |          86.3 + 63.4          |          10.4 + 3.3           |                77.2                 |                65.8                |

## Download

Install `huggingface-cli`

```bash
brew install huggingface-cli
```

```bash
huggingface-cli download --local-dir models apple/coreml-mobileclip
```

## Citation

**[MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training](https://arxiv.org/pdf/2311.17049.pdf). (CVPR 2024)**
*Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel.*

```bibtex
@InProceedings{mobileclip2024,
  author = {Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel},
  title = {MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2024},
}
```