v0.65.0
Browse filesSee https://github.com/qualcomm/ai-hub-models/releases/v0.65.0 for changelog.
- README.md +6 -36
- release_assets.json +4 -21
README.md
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@@ -15,7 +15,7 @@ pipeline_tag: object-detection
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OWL-ViT (Open-World Localization with Vision Transformers) is an open-vocabulary object detector that uses a CLIP-based ViT-B/32 backbone. Given an image and one or more free-form text queries, the model predicts bounding boxes and confidence scores for each query.
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This is based on the implementation of OWL-ViT found [here](https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit).
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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/v0.
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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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| Runtime | Precision | Chipset | SDK Versions | Download |
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|---|---|---|---|---|
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| ONNX | float | Universal | QAIRT 2.50, ONNX Runtime 1.30.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.
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| QNN_DLC | w8a16 | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.64.0/owl_vit-qnn_dlc-w8a16.zip)
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| TFLITE | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.64.0/owl_vit-tflite-float.zip)
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For more device-specific assets and performance metrics, visit **[OWL-ViT on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/owl_vit)**.
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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/v0.
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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 [OWL-ViT on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.
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## Model Details
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| OWL-ViT | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 51.137 ms | 6 - 16 MB | NPU
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| OWL-ViT | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 44.41 ms | 295 - 295 MB | NPU
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| OWL-ViT | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 26.03 ms | 2 - 478 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 7.541 ms | 1 - 576 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 10.809 ms | 1 - 560 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Snapdragon® X2 Elite | 8.263 ms | 3 - 3 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Snapdragon® X Elite | 21.342 ms | 174 - 174 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 14.727 ms | 4 - 725 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Snapdragon® 8 Gen 1 Mobile | 25.699 ms | 0 - 726 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS6490 | 84.949 ms | 3 - 9 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 19.894 ms | 1 - 8 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 20.74 ms | 0 - 192 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Qualcomm® QCS8450 | 25.699 ms | 0 - 726 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 20.865 ms | 0 - 7 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 21.342 ms | 174 - 174 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-6690 | 219.274 ms | 6 - 792 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-7790 | 34.032 ms | 6 - 594 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 10.809 ms | 1 - 560 MB | NPU
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| OWL-ViT | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 34.032 ms | 6 - 594 MB | NPU
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| OWL-ViT | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 19.807 ms | 7 - 444 MB | NPU
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| OWL-ViT | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 25.537 ms | 5 - 441 MB | NPU
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| OWL-ViT | QNN_DLC | float | Snapdragon® X2 Elite | 19.282 ms | 7 - 7 MB | NPU
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| OWL-ViT | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 43.139 ms | 7 - 7 MB | NPU
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| OWL-ViT | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 25.537 ms | 5 - 441 MB | NPU
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| OWL-ViT | QNN_DLC | float | Qualcomm® SA8295P | 53.656 ms | 0 - 407 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 8.737 ms | 3 - 525 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 11.535 ms | 3 - 504 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 9.559 ms | 3 - 3 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Snapdragon® X Elite | 23.241 ms | 3 - 3 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 15.718 ms | 3 - 663 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-8275 | 21.195 ms | 3 - 10 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 22.447 ms | 4 - 7 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Qualcomm® SA8650P | 95.803 ms | 0 - 747 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Qualcomm® SA8255P | 95.803 ms | 0 - 747 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-9075 | 22.099 ms | 5 - 11 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ IQ-X7181 | 23.241 ms | 3 - 3 MB | NPU
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| OWL-ViT | QNN_DLC | w8a16 | Qualcomm® Dragonwing™ Q-8750 | 11.535 ms | 3 - 504 MB | NPU
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| OWL-ViT | TFLITE | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 11.732 ms | 0 - 445 MB | NPU
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| OWL-ViT | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 17.129 ms | 0 - 446 MB | NPU
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| OWL-ViT | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 22.271 ms | 0 - 601 MB | NPU
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OWL-ViT (Open-World Localization with Vision Transformers) is an open-vocabulary object detector that uses a CLIP-based ViT-B/32 backbone. Given an image and one or more free-form text queries, the model predicts bounding boxes and confidence scores for each query.
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This is based on the implementation of OWL-ViT found [here](https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit).
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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/v0.65.0/src/qai_hub_models/models/owl_vit) 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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| Runtime | Precision | Chipset | SDK Versions | Download |
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|---|---|---|---|---|
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| ONNX | float | Universal | QAIRT 2.50, ONNX Runtime 1.30.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.65.0/owl_vit-onnx-float.zip)
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| QNN_DLC | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.65.0/owl_vit-qnn_dlc-float.zip)
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| TFLITE | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.65.0/owl_vit-tflite-float.zip)
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For more device-specific assets and performance metrics, visit **[OWL-ViT on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/owl_vit)**.
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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/v0.65.0/src/qai_hub_models/models/owl_vit) 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 [OWL-ViT on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.65.0/src/qai_hub_models/models/owl_vit) for usage instructions.
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## Model Details
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| OWL-ViT | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 51.137 ms | 6 - 16 MB | NPU
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| OWL-ViT | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 44.41 ms | 295 - 295 MB | NPU
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| OWL-ViT | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 26.03 ms | 2 - 478 MB | NPU
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| OWL-ViT | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 19.807 ms | 7 - 444 MB | NPU
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| OWL-ViT | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 25.537 ms | 5 - 441 MB | NPU
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| OWL-ViT | QNN_DLC | float | Snapdragon® X2 Elite | 19.282 ms | 7 - 7 MB | NPU
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| OWL-ViT | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 43.139 ms | 7 - 7 MB | NPU
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| OWL-ViT | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 25.537 ms | 5 - 441 MB | NPU
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| OWL-ViT | QNN_DLC | float | Qualcomm® SA8295P | 53.656 ms | 0 - 407 MB | NPU
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| OWL-ViT | TFLITE | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 11.732 ms | 0 - 445 MB | NPU
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| OWL-ViT | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 17.129 ms | 0 - 446 MB | NPU
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| OWL-ViT | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 22.271 ms | 0 - 601 MB | NPU
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release_assets.json
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{
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"version": "0.
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"precisions": {
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"float": {
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"universal_assets": {
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"qairt": "2.50.0.260828221209",
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"onnx_runtime": "1.30.0"
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},
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.
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},
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"qnn_dlc": {
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"tool_versions": {
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"qairt": "2.50.0.260828221209"
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},
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.
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},
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"tflite": {
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"tool_versions": {
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"qairt": "2.50.0.260828221209"
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},
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.
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}
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}
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"w8a16": {
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"universal_assets": {
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"onnx": {
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"tool_versions": {
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"qairt": "2.50.0.260828221209",
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"onnx_runtime": "1.27.1"
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.64.0/owl_vit-onnx-w8a16.zip"
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},
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"qnn_dlc": {
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"tool_versions": {
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"qairt": "2.50.0.260828221209"
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},
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.64.0/owl_vit-qnn_dlc-w8a16.zip"
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}
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}
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}
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{
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"version": "0.65.0",
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"precisions": {
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"float": {
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"universal_assets": {
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"qairt": "2.50.0.260828221209",
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"onnx_runtime": "1.30.0"
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},
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.65.0/owl_vit-onnx-float.zip"
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},
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"qnn_dlc": {
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"tool_versions": {
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"qairt": "2.50.0.260828221209"
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},
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.65.0/owl_vit-qnn_dlc-float.zip"
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},
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"tflite": {
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"tool_versions": {
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"qairt": "2.50.0.260828221209"
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},
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/owl_vit/releases/v0.65.0/owl_vit-tflite-float.zip"
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