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OpenAI-Clip

Image Classification
PyTorch
LiteRT
foundation
android
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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.

This repository provides scripts to run OpenAI-Clip on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.image_classification
  • Model Stats:
    • Model checkpoint: ViT-B/16
    • Image input resolution: 224x224
    • Text context length: 77
    • Number of parameters: 150M
    • Model size (float): 571 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
OpenAI-Clip float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 63.06 ms 0 - 494 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 59.944 ms 1 - 568 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 25.379 ms 0 - 502 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 26.32 ms 0 - 528 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 22.346 ms 0 - 29 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 21.443 ms 0 - 43 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 22.001 ms 1 - 29 MB NPU OpenAI-Clip.onnx.zip
OpenAI-Clip float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 25.238 ms 0 - 493 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 24.111 ms 1 - 568 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float SA7255P ADP Qualcomm® SA7255P TFLITE 63.06 ms 0 - 494 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float SA7255P ADP Qualcomm® SA7255P QNN_DLC 59.944 ms 1 - 568 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 22.107 ms 0 - 28 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 21.238 ms 0 - 39 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float SA8295P ADP Qualcomm® SA8295P TFLITE 28.421 ms 0 - 485 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float SA8295P ADP Qualcomm® SA8295P QNN_DLC 26.825 ms 1 - 558 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 22.106 ms 0 - 34 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 21.328 ms 0 - 40 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float SA8775P ADP Qualcomm® SA8775P TFLITE 25.238 ms 0 - 493 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float SA8775P ADP Qualcomm® SA8775P QNN_DLC 24.111 ms 1 - 568 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 15.323 ms 0 - 500 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 15.354 ms 1 - 575 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 15.187 ms 15 - 585 MB NPU OpenAI-Clip.onnx.zip
OpenAI-Clip float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile TFLITE 12.309 ms 0 - 493 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile QNN_DLC 11.779 ms 0 - 549 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile ONNX 12.223 ms 0 - 550 MB NPU OpenAI-Clip.onnx.zip
OpenAI-Clip float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile TFLITE 9.957 ms 0 - 491 MB NPU OpenAI-Clip.tflite
OpenAI-Clip float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile QNN_DLC 9.625 ms 0 - 497 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile ONNX 10.125 ms 0 - 498 MB NPU OpenAI-Clip.onnx.zip
OpenAI-Clip float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 21.948 ms 1478 - 1478 MB NPU OpenAI-Clip.dlc
OpenAI-Clip float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 22.605 ms 295 - 295 MB NPU OpenAI-Clip.onnx.zip

Installation

Install the package via pip:

# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
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 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.

qai-hub configure --api_token API_TOKEN

Navigate to 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.

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.
python -m qai_hub_models.models.openai_clip.export

How does this work?

This export script leverages Qualcomm® AI Hub 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.

import torch

import qai_hub as hub
from qai_hub_models.models.openai_clip import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S25")

# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()

pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])

# Compile model on a specific device
compile_job = hub.submit_compile_job(
    model=pt_model,
    device=device,
    input_specs=torch_model.get_input_spec(),
)

# Get target model to run on-device
target_model = 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.

profile_job = hub.submit_profile_job(
    model=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.

input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
    model=target_model,
    device=device,
    inputs=input_data,
)
    on_device_output = 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.

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app 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. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of OpenAI-Clip can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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