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---
license: apple-ascl
---
## MobileCLIP CoreML Models
The models described here correspond to the CoreML conversion of the original MobileCLIP models from Apple. For more details, refer to [MobileCLIP on HuggingFace](https://huggingface.co./apple/mobileclip_b_timm) and [MobileCLIP on GitHub](https://github.com/apple/ml-mobileclip).
The models are separated for each subarchitecture:
- **MobileCLIP-S0**: This subarchitecture is designed for lightweight and fast inference, making it suitable for edge devices with limited computational resources.
- **MobileCLIP-S1**: This subarchitecture offers a balance between model complexity and performance, providing a good trade-off for various applications.
- **MobileCLIP-S2**: This subarchitecture focuses on achieving higher accuracy, ideal for applications where performance can be slightly compromised for better results.
- **MobileCLIP-B**: This subarchitecture aims at delivering the highest possible accuracy, optimized for environments with ample computational resources.
Each subarchitecture contains a TextEncoder and ImageEncoder that are separated into CoreML models for each subarchitecture:
| Model | CLIP Text | CLIP Image |
|:----------------------------------------------------------|:-------------------------|:----------------------------|
| MobileCLIP-S0 | clip_text_s0.mlpackage | clip_image_s0.mlpackage |
| MobileCLIP-S1 | clip_text_s1.mlpackage | clip_image_s1.mlpackage |
| MobileCLIP-S2 | clip_text_s2.mlpackage | clip_image_s2.mlpackage |
| MobileCLIP-B | clip_text_B.mlpackage | clip_image_B.mlpackage |
For detailed implementation and architecture specifics, refer to the [MobileCLIP GitHub repository](https://github.com/apple/ml-mobileclip).
## Example Usage
An example of using these CoreML models in a Swift application for iOS can be found in the [CLIP-Finder](https://github.com/fguzman82/CLIP-Finder2) project.
**CoreML Parameters:**
| Model | Input Name | Input Shape | Input DataType | Output Name | Output Shape | Output DataType |
|:---------|:-------------|:------------|:---------------|:-------------------|:-------------|:----------------|
| CLIP Text| input_text | (1,77) | INT32 | output_embeddings | (1,512) | FLOAT16 |
| Model | Input Name | Input Width | Input Height | Input ColorSpace | Output Name | Output Shape | Output DataType |
|:---------|:-------------|:------------|:-------------|:-----------------|:-------------------|:-------------|:----------------|
| CLIP Image| input_image | 256 | 256 | RGB | output_embeddings | (1,512) | FLOAT16 |
## CoreML Profile (Benchmark) on Apple M1
| Prediction Times Apple M1 | CPU + ANE | CPU + GPU | CPU Only |
|:--------------------------|:---------:|:---------:|:--------:|
| clip_image_s0 | 1.4ms | 7.4ms | 12.7ms |
| clip_image_s1 | 2.1ms | 13.3ms | 21.8ms |
| clip_image_s2 | 3.0ms | 19.0ms | 28.5ms |
| clip_image_b | 12.4ms | 36.2ms | 38.1ms |
| | | | |
| clip_text_s0 | 1.1ms | 4.1ms | 4.8ms |
| clip_text_s1 | 2.0ms | 7.1ms | 9.5ms |
| clip_text_s2 | 2.0ms | 7.1ms | 10ms |
| clip_text_b | 2.0ms | 7.2ms | 9.8ms |
The profile was conducted using this tool: [CoreMLProfiler](https://github.com/fguzman82/CoreMLProfiler).
*These are example scripts for performing the conversion to CoreML*
1. **CLIPImageModel to CoreML** [](https://colab.research.google.com/drive/1ZHMzsJyAukBa4Jryv4Tmc_BOBmbQAjxf?usp=sharing)
- This notebook demonstrates the process of converting a CLIP image model to CoreML format.
2. **CLIPTextModel to CoreML** [](https://colab.research.google.com/drive/1PxzB8M0h2bf-uYpw7fIZImpGSVXUI7Ie?usp=sharing)
- This notebook demonstrates the process of converting a CLIP text model to CoreML format. |