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--- |
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license: other |
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license_name: apple-ascl |
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license_link: https://github.com/apple/ml-mobileclip/blob/main/LICENSE_weights_data |
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library_name: mobileclip |
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--- |
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# MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training |
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MobileCLIP was introduced in [MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training |
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](https://arxiv.org/pdf/2311.17049.pdf) (CVPR 2024), by Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel. |
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This repository contains the **MobileCLIP-S2** checkpoint. |
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### Highlights |
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* 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. |
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* `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. |
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* `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). |
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## Checkpoints |
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| 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 | |
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|:----------------------------------------------------------|:----------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------------:|:----------------------------------:| |
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| [MobileCLIP-S0](https://hf.co/pcuenq/MobileCLIP-S0) | 13 | 11.4 + 42.4 | 1.5 + 1.6 | 67.8 | 58.1 | |
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| [MobileCLIP-S1](https://hf.co/pcuenq/MobileCLIP-S1) | 13 | 21.5 + 63.4 | 2.5 + 3.3 | 72.6 | 61.3 | |
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| [MobileCLIP-S2](https://hf.co/pcuenq/MobileCLIP-S2) | 13 | 35.7 + 63.4 | 3.6 + 3.3 | 74.4 | 63.7 | |
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| [MobileCLIP-B](https://hf.co/pcuenq/MobileCLIP-B) | 13 | 86.3 + 63.4 | 10.4 + 3.3 | 76.8 | 65.2 | |
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| [MobileCLIP-B (LT)](https://hf.co/pcuenq/MobileCLIP-B-LT) | 36 | 86.3 + 63.4 | 10.4 + 3.3 | 77.2 | 65.8 | |
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## How to Use |
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First, download the desired checkpoint visiting one of the links in the table above, then click the `Files and versions` tab, and download the PyTorch checkpoint. |
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For programmatic downloading, if you have `huggingface_hub` installed, you can also run: |
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``` |
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huggingface-cli download pcuenq/MobileCLIP-S2 |
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``` |
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Then, install [`ml-mobileclip`](https://github.com/apple/ml-mobileclip) by following the instructions in the repo. It uses an API similar to [`open_clip`'s](https://github.com/mlfoundations/open_clip). |
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You can run inference with a code snippet like the following: |
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```py |
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import torch |
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from PIL import Image |
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import mobileclip |
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model, _, preprocess = mobileclip.create_model_and_transforms('mobileclip_s2', pretrained='/path/to/mobileclip_s2.pt') |
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tokenizer = mobileclip.get_tokenizer('mobileclip_s2') |
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image = preprocess(Image.open("docs/fig_accuracy_latency.png").convert('RGB')).unsqueeze(0) |
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text = tokenizer(["a diagram", "a dog", "a cat"]) |
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with torch.no_grad(), torch.cuda.amp.autocast(): |
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image_features = model.encode_image(image) |
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text_features = model.encode_text(text) |
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image_features /= image_features.norm(dim=-1, keepdim=True) |
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text_features /= text_features.norm(dim=-1, keepdim=True) |
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text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) |
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print("Label probs:", text_probs) |
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``` |
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