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LICENSE

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+Disclaimer: IMPORTANT: This Apple Machine Learning Research Model is
+specifically developed and released by Apple Inc. ("Apple") for the sole purpose
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README.md

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+---
+license: apple-amlr
+license_name: apple-ascl
+license_link: https://github.com/apple/ml-mobileclip/blob/main/LICENSE_weights_data
+library_name: mobileclip
+---
+
+# MobileCLIP2: Improving Multi-Modal Reinforced Training
+
+MobileCLIP2 was introduced in [MobileCLIP2: Improving Multi-Modal Reinforced Training]() (TMLR 2025), by Fartash Faghri, Pavan Kumar Anasosalu Vasu, Cem Koc, Vaishaal Shankar, Alexander T Toshev, Oncel Tuzel, Hadi Pouransari.
+
+This repository contains the **CoCa** checkpoint pretrained on DFN-2B dataset and fine-tuned on varying datasets.
+
+![MobileCLIP2 Performance Figure](fig_accuracy_latency_v2.png)
+
+### Highlights
+
+* `MobileCLIP2-S4` matches the accuracy of SigLIP-SO400M/14 with 2x fewer parameters and surpasses DFN ViT-L/14 at 2.5x lower latency measured on iPhone12 Pro Max.
+* `MobileCLIP-S3/S4` are our new architectures trained on MobileCLIP’s training dataset, DataCompDR-1B (dashed lines).
+* 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 |
+|:----------------------------------------------------------|:----------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------------:|:----------------------------------:|
+| [MobileCLIP2-S0](https://hf.co/apple/MobileCLIP2-S0)      |           13           |          11.4 + 42.4          |           1.5 + 1.6           |               71.5                 |                59.7                 |
+| [MobileCLIP2-S2](https://hf.co/apple/MobileCLIP2-S2)      |           13           |          35.7 + 63.4          |           3.6 + 3.3           |               77.2                 |                64.1                 |
+| [MobileCLIP2-B](https://hf.co/apple/MobileCLIP2-B)        |           13           |          86.3 + 63.4          |          10.4 + 3.3           |               79.4                 |                65.8                 |
+| [MobileCLIP2-S3](https://hf.co/apple/MobileCLIP2-S3)      |           13           |         125.1 + 123.6         |           8.0 + 6.6           |               80.7                 |                66.8                 |
+| [MobileCLIP2-L/14](https://hf.co/apple/MobileCLIP2-L-14)  |           13           |         304.3 + 123.6         |          57.9 + 6.6           |               81.9                 |                67.8                 |
+| [MobileCLIP2-S4](https://hf.co/apple/MobileCLIP2-S4)      |           13           |         321.6 + 123.6         |          19.6 + 6.6           |               81.9                 |                67.5                 |
+| [MobileCLIP-S0](https://hf.co/apple/MobileCLIP-S0)        |           13           |          11.4 + 42.4          |           1.5 + 1.6           |               67.8                 |                58.1                 |
+| [MobileCLIP-S1](https://hf.co/apple/MobileCLIP-S1)        |           13           |          21.5 + 63.4          |           2.5 + 3.3           |               72.6                 |                61.3                 |
+| [MobileCLIP-S2](https://hf.co/apple/MobileCLIP-S2)        |           13           |          35.7 + 63.4          |           3.6 + 3.3           |               74.4                 |                63.7                 |
+| [MobileCLIP-B](https://hf.co/apple/MobileCLIP-B)          |           13           |          86.3 + 63.4          |          10.4 + 3.3           |               76.8                 |                65.2                 |
+| [MobileCLIP-B (LT)](https://hf.co/apple/MobileCLIP-B-LT)  |           36           |          86.3 + 63.4          |          10.4 + 3.3           |               77.2                 |                65.8                 |
+| [MobileCLIP-S3](https://hf.co/apple/MobileCLIP-S3)        |           13           |         125.1 + 123.6         |           8.0 + 6.6           |               78.3                 |                66.3                 |
+| [MobileCLIP-L/14](https://hf.co/apple/MobileCLIP-L-14)    |           13           |         304.3 + 123.6         |          57.9 + 6.6           |               79.5                 |                66.9                 |
+| [MobileCLIP-S4](https://hf.co/apple/MobileCLIP-S4)        |           13           |         321.6 + 123.6         |          19.6 + 6.6           |               79.4                 |                68.1                 |
+
+
+## How to Use
+
+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.
+For programmatic downloading, if you have `huggingface_hub` installed, you can also run:
+
+```
+hf download apple/mobileclip2_coca_dfn2b_s13b_<finetune-dataset>_context<length>
+``` 
+
+For models length with context lengths 128/256, copy `config.json` to `src/open_clip/model_configs/coca_ViT-L-14-context$len.json` and change the model name in below example to `coca_ViT-L-14-context$len`.
+
+```py
+import torch
+import open_clip
+from PIL import Image
+
+model, _, preprocess = open_clip.create_model_and_transforms('coca_ViT-L-14', pretrained='/path/to/mobileclip2_coca.pt')
+model.eval()
+
+image = preprocess(Image.open("docs/fig_accuracy_latency.png").convert('RGB')).unsqueeze(0)
+
+with torch.no_grad(), torch.cuda.amp.autocast():
+    syn_text = model.generate(
+        image,
+        generation_type="top_p",
+        top_p=0.9,
+        fixed_output_length=True
+    )[0]
+    syn_text = open_clip.decode(syn_text).split("<end_of_text>")[0].split("<start_of_text>")[-1].split(".")[0].rstrip()
+print("Caption:", syn_text)
+```

config.json

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+{
+    "embed_dim": 768,
+    "vision_cfg": {
+        "image_size": 224,
+        "layers": 24,
+        "width": 1024,
+        "patch_size": 14,
+        "attentional_pool": true,
+        "attn_pooler_heads": 8,
+        "output_tokens": true
+    },
+    "text_cfg": {
+        "context_length": 76,
+        "vocab_size": 49408,
+        "width": 768,
+        "heads": 12,
+        "layers": 12,
+        "embed_cls": true,
+        "output_tokens": true
+    },
+    "multimodal_cfg": {
+        "context_length": 76,
+        "vocab_size": 49408,
+        "width": 768,
+        "heads": 12,
+        "layers": 12,
+        "attn_pooler_heads": 12
+    },
+    "custom_text": true
+}

mobileclip2_coca_dfn2b_s13b_dci-complete_s12m_context77.pt

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