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OpenAI-Clip

Image Classification
PyTorch
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android
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v0.49.1

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See https://github.com/qualcomm/ai-hub-models/releases/v0.49.1 for changelog.

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  1. README.md +38 -32
README.md CHANGED
@@ -15,7 +15,7 @@ pipeline_tag: image-classification
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  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.
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  This is based on the implementation of OpenAI-Clip found [here](https://github.com/openai/CLIP/).
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- This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/openai_clip) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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  Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
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@@ -24,19 +24,25 @@ There are two ways to deploy this model on your device:
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  ### Option 1: Download Pre-Exported Models
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- Download pre-exported model assets from **[OpenAI-Clip on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/openai_clip)**.
 
 
 
 
 
 
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  ### Option 2: Export with Custom Configurations
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- Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/openai_clip) Python library to compile and export the model with your own:
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  - Custom weights (e.g., fine-tuned checkpoints)
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  - Custom input shapes
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  - Target device and runtime configurations
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  This option is ideal if you need to customize the model beyond the default configuration provided here.
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- See our repository for [OpenAI-Clip on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/openai_clip) for usage instructions.
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  ## Model Details
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@@ -52,35 +58,35 @@ See our repository for [OpenAI-Clip on GitHub](https://github.com/qualcomm/ai-hu
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  ## Performance Summary
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  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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  |---|---|---|---|---|---|---
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- | OpenAI-Clip | ONNX | float | Snapdragon® X2 Elite | 7.205 ms | 291 - 291 MB | NPU
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- | OpenAI-Clip | ONNX | float | Snapdragon® X Elite | 16.481 ms | 291 - 291 MB | NPU
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- | OpenAI-Clip | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 11.304 ms | 1 - 564 MB | NPU
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- | OpenAI-Clip | ONNX | float | Qualcomm® QCS8550 (Proxy) | 15.726 ms | 0 - 335 MB | NPU
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- | OpenAI-Clip | ONNX | float | Qualcomm® QCS9075 | 20.388 ms | 0 - 4 MB | NPU
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- | OpenAI-Clip | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 9.086 ms | 1 - 533 MB | NPU
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- | OpenAI-Clip | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 6.999 ms | 1 - 496 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Snapdragon® X2 Elite | 9.095 ms | 1 - 1 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Snapdragon® X Elite | 18.846 ms | 1 - 1 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 12.55 ms | 0 - 554 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 56.004 ms | 1 - 505 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 17.857 ms | 1 - 3 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Qualcomm® SA8775P | 20.945 ms | 1 - 504 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Qualcomm® QCS9075 | 21.217 ms | 3 - 5 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 21.0 ms | 0 - 502 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Qualcomm® SA7255P | 56.004 ms | 1 - 505 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Qualcomm® SA8295P | 22.083 ms | 1 - 497 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 10.522 ms | 1 - 516 MB | NPU
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- | OpenAI-Clip | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.338 ms | 0 - 486 MB | NPU
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- | OpenAI-Clip | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 11.082 ms | 0 - 559 MB | NPU
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- | OpenAI-Clip | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 52.065 ms | 0 - 508 MB | NPU
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- | OpenAI-Clip | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 15.604 ms | 0 - 3 MB | NPU
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- | OpenAI-Clip | TFLITE | float | Qualcomm® SA8775P | 18.667 ms | 0 - 508 MB | NPU
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- | OpenAI-Clip | TFLITE | float | Qualcomm® QCS9075 | 20.359 ms | 0 - 294 MB | NPU
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- | OpenAI-Clip | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 20.309 ms | 0 - 502 MB | NPU
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- | OpenAI-Clip | TFLITE | float | Qualcomm® SA7255P | 52.065 ms | 0 - 508 MB | NPU
 
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  | OpenAI-Clip | TFLITE | float | Qualcomm® SA8295P | 21.252 ms | 0 - 495 MB | NPU
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- | OpenAI-Clip | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 9.021 ms | 0 - 517 MB | NPU
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- | OpenAI-Clip | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 6.928 ms | 0 - 496 MB | NPU
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  ## License
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  * The license for the original implementation of OpenAI-Clip can be found
 
15
  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.
16
 
17
  This is based on the implementation of OpenAI-Clip found [here](https://github.com/openai/CLIP/).
18
+ This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/openai_clip) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
19
 
20
  Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
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  ### Option 1: Download Pre-Exported Models
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+ Below are pre-exported model assets ready for deployment.
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+
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+ | Runtime | Precision | Chipset | SDK Versions | Download |
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+ |---|---|---|---|---|
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+ | TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/openai_clip/releases/v0.49.1/openai_clip-tflite-float.zip)
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+
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+ For more device-specific assets and performance metrics, visit **[OpenAI-Clip on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/openai_clip)**.
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  ### Option 2: Export with Custom Configurations
37
 
38
+ Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/openai_clip) Python library to compile and export the model with your own:
39
  - Custom weights (e.g., fine-tuned checkpoints)
40
  - Custom input shapes
41
  - Target device and runtime configurations
42
 
43
  This option is ideal if you need to customize the model beyond the default configuration provided here.
44
 
45
+ See our repository for [OpenAI-Clip on GitHub](https://github.com/qualcomm/ai-hub-models/tree/v0.49.1/qai_hub_models/models/openai_clip) for usage instructions.
46
 
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  ## Model Details
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  ## Performance Summary
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  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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  |---|---|---|---|---|---|---
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+ | OpenAI-Clip | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.002 ms | 1 - 497 MB | NPU
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+ | OpenAI-Clip | ONNX | float | Snapdragon® X2 Elite | 7.214 ms | 291 - 291 MB | NPU
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+ | OpenAI-Clip | ONNX | float | Snapdragon® X Elite | 16.49 ms | 291 - 291 MB | NPU
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+ | OpenAI-Clip | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 11.232 ms | 0 - 550 MB | NPU
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+ | OpenAI-Clip | ONNX | float | Qualcomm® QCS8550 (Proxy) | 15.861 ms | 0 - 325 MB | NPU
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+ | OpenAI-Clip | ONNX | float | Qualcomm® QCS9075 | 20.256 ms | 0 - 4 MB | NPU
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+ | OpenAI-Clip | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 9.013 ms | 1 - 534 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.339 ms | 1 - 487 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Snapdragon® X2 Elite | 9.132 ms | 1 - 1 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Snapdragon® X Elite | 18.815 ms | 1 - 1 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 12.527 ms | 0 - 551 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 56.048 ms | 1 - 506 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 17.844 ms | 1 - 586 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Qualcomm® SA8775P | 20.943 ms | 1 - 504 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Qualcomm® QCS9075 | 21.165 ms | 1 - 3 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 21.001 ms | 0 - 502 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Qualcomm® SA7255P | 56.048 ms | 1 - 506 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Qualcomm® SA8295P | 22.062 ms | 0 - 496 MB | NPU
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+ | OpenAI-Clip | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 10.54 ms | 1 - 515 MB | NPU
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+ | OpenAI-Clip | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 6.925 ms | 0 - 495 MB | NPU
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+ | OpenAI-Clip | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 11.082 ms | 0 - 556 MB | NPU
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+ | OpenAI-Clip | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 52.058 ms | 0 - 508 MB | NPU
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+ | OpenAI-Clip | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 15.273 ms | 0 - 3 MB | NPU
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+ | OpenAI-Clip | TFLITE | float | Qualcomm® SA8775P | 18.655 ms | 0 - 507 MB | NPU
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+ | OpenAI-Clip | TFLITE | float | Qualcomm® QCS9075 | 20.329 ms | 0 - 294 MB | NPU
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+ | OpenAI-Clip | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 20.243 ms | 0 - 501 MB | NPU
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+ | OpenAI-Clip | TFLITE | float | Qualcomm® SA7255P | 52.058 ms | 0 - 508 MB | NPU
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  | OpenAI-Clip | TFLITE | float | Qualcomm® SA8295P | 21.252 ms | 0 - 495 MB | NPU
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+ | OpenAI-Clip | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 9.056 ms | 0 - 522 MB | NPU
 
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  ## License
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  * The license for the original implementation of OpenAI-Clip can be found