README.md

---
library_name: pytorch
license: mit
pipeline_tag: image-classification
tags:
- foundation
- android

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/openai_clip/web-assets/model_demo.png)

# OpenAI-Clip: Optimized for Mobile Deployment
## Multi-modal foundational model for vision and language tasks like image/text similarity and for zero-shot image classification

Contrastive Language-Image Pre-Training (CLIP) uses a ViT like transformer to get visual features and a causal language model to get the text features. Both the text and visual features can then be used for a variety of zero-shot learning tasks.

This model is an implementation of OpenAI-Clip found [here]({source_repo}).
This repository provides scripts to run OpenAI-Clip on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/openai_clip).


### Model Details

- **Model Type:** Image classification
- **Model Stats:**
  - Model checkpoint: ViT-B/16
  - Image input resolution: 224x224
  - Text context length: 77
  - Number of parameters (CLIPTextEncoder): 76.0M
  - Model size (CLIPTextEncoder): 290 MB
  - Number of parameters (CLIPImageEncoder): 115M
  - Model size (CLIPImageEncoder): 437 MB

| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| CLIPTextEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 5.779 ms | 0 - 3 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) |
| CLIPTextEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 4.774 ms | 0 - 16 MB | FP16 | NPU | [OpenAI-Clip.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.so) |
| CLIPTextEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 35.403 ms | 0 - 130 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.onnx) |
| CLIPTextEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 4.079 ms | 0 - 194 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) |
| CLIPTextEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 3.405 ms | 0 - 66 MB | FP16 | NPU | [OpenAI-Clip.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.so) |
| CLIPTextEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 26.223 ms | 0 - 534 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.onnx) |
| CLIPTextEncoder | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 5.717 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) |
| CLIPTextEncoder | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 4.856 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
| CLIPTextEncoder | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 5.711 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) |
| CLIPTextEncoder | SA8255 (Proxy) | SA8255P Proxy | QNN | 4.794 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
| CLIPTextEncoder | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 5.652 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) |
| CLIPTextEncoder | SA8775 (Proxy) | SA8775P Proxy | QNN | 4.897 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
| CLIPTextEncoder | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 5.683 ms | 0 - 287 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) |
| CLIPTextEncoder | SA8650 (Proxy) | SA8650P Proxy | QNN | 4.903 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
| CLIPTextEncoder | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 6.593 ms | 0 - 168 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) |
| CLIPTextEncoder | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 5.491 ms | 0 - 66 MB | FP16 | NPU | Use Export Script |
| CLIPTextEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 3.963 ms | 0 - 109 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) |
| CLIPTextEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 3.266 ms | 0 - 65 MB | FP16 | NPU | Use Export Script |
| CLIPTextEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 23.78 ms | 0 - 319 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.onnx) |
| CLIPTextEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 5.196 ms | 0 - 0 MB | FP16 | NPU | Use Export Script |
| CLIPTextEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 38.329 ms | 126 - 126 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.onnx) |
| CLIPImageEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 38.384 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) |
| CLIPImageEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 27.206 ms | 0 - 56 MB | FP16 | NPU | [OpenAI-Clip.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.so) |
| CLIPImageEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 174.036 ms | 0 - 194 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.onnx) |
| CLIPImageEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 33.247 ms | 0 - 666 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) |
| CLIPImageEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 24.164 ms | 1 - 170 MB | FP16 | NPU | [OpenAI-Clip.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.so) |
| CLIPImageEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 118.868 ms | 1 - 3571 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.onnx) |
| CLIPImageEncoder | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 37.343 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) |
| CLIPImageEncoder | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 22.015 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
| CLIPImageEncoder | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 37.324 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) |
| CLIPImageEncoder | SA8255 (Proxy) | SA8255P Proxy | QNN | 22.477 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
| CLIPImageEncoder | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 36.58 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) |
| CLIPImageEncoder | SA8775 (Proxy) | SA8775P Proxy | QNN | 22.644 ms | 0 - 1 MB | FP16 | NPU | Use Export Script |
| CLIPImageEncoder | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 36.958 ms | 0 - 3 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) |
| CLIPImageEncoder | SA8650 (Proxy) | SA8650P Proxy | QNN | 22.477 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
| CLIPImageEncoder | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 37.123 ms | 0 - 549 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) |
| CLIPImageEncoder | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 30.382 ms | 0 - 170 MB | FP16 | NPU | Use Export Script |
| CLIPImageEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 25.495 ms | 0 - 460 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) |
| CLIPImageEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 17.137 ms | 1 - 172 MB | FP16 | NPU | Use Export Script |
| CLIPImageEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 17.137 ms | 1 - 172 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.onnx) |
| CLIPImageEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 22.135 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
| CLIPImageEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 162.155 ms | 188 - 188 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.onnx) |




## Installation

This model can be installed as a Python package via pip.

```bash
pip install "qai-hub-models[openai_clip]"
```



## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.

With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.



## Demo off target

The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.

```bash
python -m qai_hub_models.models.openai_clip.demo
```

The above demo runs a reference implementation of pre-processing, model
inference, and post processing.

**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.openai_clip.demo
```


### Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.

```bash
python -m qai_hub_models.models.openai_clip.export
```
```
Profiling Results
------------------------------------------------------------
CLIPTextEncoder
Device                          : Samsung Galaxy S23 (13)  
Runtime                         : TFLITE                   
Estimated inference time (ms)   : 5.8                      
Estimated peak memory usage (MB): [0, 3]                   
Total # Ops                     : 660                      
Compute Unit(s)                 : NPU (658 ops) CPU (2 ops)

------------------------------------------------------------
CLIPImageEncoder
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 38.4                   
Estimated peak memory usage (MB): [0, 2]                 
Total # Ops                     : 659                    
Compute Unit(s)                 : NPU (659 ops)          
```


## How does this work?

This [export script](https://aihub.qualcomm.com/models/openai_clip/qai_hub_models/models/OpenAI-Clip/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:

Step 1: **Compile model for on-device deployment**

To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.

```python
import torch

import qai_hub as hub
from qai_hub_models.models.openai_clip import CLIPTextEncoder,CLIPImageEncoder

# Load the model
text_encoder_model = CLIPTextEncoder.from_pretrained()
image_encoder_model = CLIPImageEncoder.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S23")

# Trace model
text_encoder_input_shape = text_encoder_model.get_input_spec()
text_encoder_sample_inputs = text_encoder_model.sample_inputs()

traced_text_encoder_model = torch.jit.trace(text_encoder_model, [torch.tensor(data[0]) for _, data in text_encoder_sample_inputs.items()])

# Compile model on a specific device
text_encoder_compile_job = hub.submit_compile_job(
    model=traced_text_encoder_model ,
    device=device,
    input_specs=text_encoder_model.get_input_spec(),
)

# Get target model to run on-device
text_encoder_target_model = text_encoder_compile_job.get_target_model()
# Trace model
image_encoder_input_shape = image_encoder_model.get_input_spec()
image_encoder_sample_inputs = image_encoder_model.sample_inputs()

traced_image_encoder_model = torch.jit.trace(image_encoder_model, [torch.tensor(data[0]) for _, data in image_encoder_sample_inputs.items()])

# Compile model on a specific device
image_encoder_compile_job = hub.submit_compile_job(
    model=traced_image_encoder_model ,
    device=device,
    input_specs=image_encoder_model.get_input_spec(),
)

# Get target model to run on-device
image_encoder_target_model = image_encoder_compile_job.get_target_model()

```


Step 2: **Performance profiling on cloud-hosted device**

After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud.  Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
text_encoder_profile_job = hub.submit_profile_job(
    model=text_encoder_target_model,
    device=device,
)
image_encoder_profile_job = hub.submit_profile_job(
    model=image_encoder_target_model,
    device=device,
)

```

Step 3: **Verify on-device accuracy**

To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
text_encoder_input_data = text_encoder_model.sample_inputs()
text_encoder_inference_job = hub.submit_inference_job(
    model=text_encoder_target_model,
    device=device,
    inputs=text_encoder_input_data,
)
text_encoder_inference_job.download_output_data()
image_encoder_input_data = image_encoder_model.sample_inputs()
image_encoder_inference_job = hub.submit_inference_job(
    model=image_encoder_target_model,
    device=device,
    inputs=image_encoder_input_data,
)
image_encoder_inference_job.download_output_data()

```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.

**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).




## Deploying compiled model to Android


The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
  tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
  guide to deploy the .tflite model in an Android application.


- QNN (`.so` export ): This [sample
  app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library  in an Android application.


## View on Qualcomm® AI Hub
Get more details on OpenAI-Clip's performance across various devices [here](https://aihub.qualcomm.com/models/openai_clip).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)


## License
* The license for the original implementation of OpenAI-Clip can be found [here](https://github.com/openai/CLIP/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)



## References
* [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020)
* [Source Model Implementation](https://github.com/openai/CLIP/)



## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:[email protected]).