v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- Facial-Attribute-Detection_float.dlc +0 -3
- Facial-Attribute-Detection_float.onnx.zip +0 -3
- Facial-Attribute-Detection_float.tflite +0 -3
- Facial-Attribute-Detection_w8a8.dlc +0 -3
- Facial-Attribute-Detection_w8a8.onnx.zip +0 -3
- Facial-Attribute-Detection_w8a8.tflite +0 -3
- README.md +112 -255
- tool-versions.yaml +0 -4
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README.md
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# Facial-Attribute-Detection: Optimized for
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## Comprehensive facial analysis by extracting face features
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Detects attributes (eye closeness, mask presence, eyeglasses presence, sunglasses presence) that apply to a given face. This model's architecture was developed by Qualcomm. The model was trained by Qualcomm on a proprietary dataset of faces, but can be used on any image.
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```bash
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python -m qai_hub_models.models.face_attrib_net.demo
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```
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The above demo runs a reference implementation of pre-processing, model
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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environment, please add the following to your cell (instead of the above).
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%run -m qai_hub_models.models.face_attrib_net.demo
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```
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### Run model on a cloud-hosted device
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device. This script does the following:
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* Performance check on-device on a cloud-hosted device
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* Accuracy check between PyTorch and on-device outputs.
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python -m qai_hub_models.models.face_attrib_net.export
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```
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## How does this work?
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This [export script](https://aihub.qualcomm.com/models/face_attrib_net/qai_hub_models/models/Facial-Attribute-Detection/export.py)
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
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on-device. Lets go through each step below in detail:
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Step 1: **Compile model for on-device deployment**
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in memory using the `jit.trace` and then call the `submit_compile_job` API.
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import torch
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import qai_hub as hub
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from qai_hub_models.models.face_attrib_net import Model
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# Load the model
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torch_model = Model.from_pretrained()
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# Device
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device = hub.Device("Samsung Galaxy S25")
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sample_inputs = torch_model.sample_inputs()
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pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
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# Compile model on a specific device
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compile_job = hub.submit_compile_job(
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)
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# Get target model to run on-device
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target_model = compile_job.get_target_model()
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```
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`target_model`. Note that this scripts runs the model on a device automatically
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provisioned in the cloud. Once the job is submitted, you can navigate to a
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provided job URL to view a variety of on-device performance metrics.
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spot check the output with expected output.
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AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
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## Run demo on a cloud-hosted device
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environment, please add the following to your cell (instead of the above).
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%run -m qai_hub_models.models.face_attrib_net.demo -- --eval-mode on-device
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```
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## Deploying compiled model to Android
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The models can be deployed using multiple runtimes:
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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provides instructions on how to use the `.so` shared library in an Android application.
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## View on Qualcomm® AI Hub
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Get more details on Facial-Attribute-Detection's performance across various devices [here](https://aihub.qualcomm.com/models/face_attrib_net).
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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## License
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* The license for the original implementation of Facial-Attribute-Detection can be found
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## Community
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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# Facial-Attribute-Detection: Optimized for Qualcomm Devices
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Detects attributes (eye closeness, mask presence, eyeglasses presence, sunglasses presence) that apply to a given face. This model's architecture was developed by Qualcomm. The model was trained by Qualcomm on a proprietary dataset of faces, but can be used on any image.
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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/quic/ai-hub-models/blob/main/qai_hub_models/models/face_attrib_net) 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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## Getting Started
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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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Below are pre-exported model assets ready for deployment.
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| Runtime | Precision | Chipset | SDK Versions | Download |
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|---|---|---|---|---|
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| ONNX | float | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/face_attrib_net/releases/v0.46.1/face_attrib_net-onnx-float.zip)
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| ONNX | w8a8 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/face_attrib_net/releases/v0.46.1/face_attrib_net-onnx-w8a8.zip)
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| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/face_attrib_net/releases/v0.46.1/face_attrib_net-qnn_dlc-float.zip)
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| QNN_DLC | w8a8 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/face_attrib_net/releases/v0.46.1/face_attrib_net-qnn_dlc-w8a8.zip)
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| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/face_attrib_net/releases/v0.46.1/face_attrib_net-tflite-float.zip)
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| TFLITE | w8a8 | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/face_attrib_net/releases/v0.46.1/face_attrib_net-tflite-w8a8.zip)
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For more device-specific assets and performance metrics, visit **[Facial-Attribute-Detection on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/face_attrib_net)**.
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### Option 2: Export with Custom Configurations
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Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/face_attrib_net) 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 [Facial-Attribute-Detection on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/face_attrib_net) for usage instructions.
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## Model Details
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**Model Type:** Model_use_case.object_detection
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**Model Stats:**
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- Model checkpoint: multitask_FR_state_dict.pt
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- Input resolution: 128x128
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- Number of parameters: 12.1M
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- Model size (float): 46.3 MB
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- Model size (w8a8): 12.3 MB
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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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| Facial-Attribute-Detection | ONNX | float | Snapdragon® X Elite | 0.986 ms | 22 - 22 MB | NPU
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| Facial-Attribute-Detection | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.859 ms | 0 - 145 MB | NPU
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| Facial-Attribute-Detection | ONNX | float | Qualcomm® QCS8550 (Proxy) | 1.096 ms | 0 - 29 MB | NPU
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| Facial-Attribute-Detection | ONNX | float | Qualcomm® QCS9075 | 1.503 ms | 0 - 3 MB | NPU
|
| 70 |
+
| Facial-Attribute-Detection | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.623 ms | 0 - 120 MB | NPU
|
| 71 |
+
| Facial-Attribute-Detection | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.554 ms | 0 - 120 MB | NPU
|
| 72 |
+
| Facial-Attribute-Detection | ONNX | w8a8 | Snapdragon® X Elite | 0.514 ms | 11 - 11 MB | NPU
|
| 73 |
+
| Facial-Attribute-Detection | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.475 ms | 0 - 151 MB | NPU
|
| 74 |
+
| Facial-Attribute-Detection | ONNX | w8a8 | Qualcomm® QCS6490 | 35.118 ms | 21 - 28 MB | CPU
|
| 75 |
+
| Facial-Attribute-Detection | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.615 ms | 0 - 128 MB | NPU
|
| 76 |
+
| Facial-Attribute-Detection | ONNX | w8a8 | Qualcomm® QCS9075 | 0.72 ms | 0 - 3 MB | NPU
|
| 77 |
+
| Facial-Attribute-Detection | ONNX | w8a8 | Qualcomm® QCM6690 | 16.742 ms | 20 - 30 MB | CPU
|
| 78 |
+
| Facial-Attribute-Detection | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.358 ms | 0 - 124 MB | NPU
|
| 79 |
+
| Facial-Attribute-Detection | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 12.327 ms | 19 - 28 MB | CPU
|
| 80 |
+
| Facial-Attribute-Detection | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.339 ms | 0 - 127 MB | NPU
|
| 81 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Snapdragon® X Elite | 0.999 ms | 0 - 0 MB | NPU
|
| 82 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.652 ms | 0 - 81 MB | NPU
|
| 83 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 4.312 ms | 0 - 53 MB | NPU
|
| 84 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.867 ms | 0 - 2 MB | NPU
|
| 85 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Qualcomm® SA8775P | 1.375 ms | 0 - 57 MB | NPU
|
| 86 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Qualcomm® QCS9075 | 1.209 ms | 2 - 4 MB | NPU
|
| 87 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1.339 ms | 0 - 77 MB | NPU
|
| 88 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Qualcomm® SA7255P | 4.312 ms | 0 - 53 MB | NPU
|
| 89 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Qualcomm® SA8295P | 1.521 ms | 0 - 48 MB | NPU
|
| 90 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.517 ms | 0 - 58 MB | NPU
|
| 91 |
+
| Facial-Attribute-Detection | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.421 ms | 0 - 58 MB | NPU
|
| 92 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Snapdragon® X Elite | 0.507 ms | 0 - 0 MB | NPU
|
| 93 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.28 ms | 0 - 63 MB | NPU
|
| 94 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 1.301 ms | 0 - 2 MB | NPU
|
| 95 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.069 ms | 0 - 47 MB | NPU
|
| 96 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.389 ms | 0 - 1 MB | NPU
|
| 97 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Qualcomm® SA8775P | 0.564 ms | 0 - 50 MB | NPU
|
| 98 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 0.466 ms | 0 - 2 MB | NPU
|
| 99 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 2.948 ms | 0 - 49 MB | NPU
|
| 100 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.633 ms | 0 - 65 MB | NPU
|
| 101 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Qualcomm® SA7255P | 1.069 ms | 0 - 47 MB | NPU
|
| 102 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Qualcomm® SA8295P | 0.802 ms | 0 - 45 MB | NPU
|
| 103 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.237 ms | 0 - 50 MB | NPU
|
| 104 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.484 ms | 0 - 46 MB | NPU
|
| 105 |
+
| Facial-Attribute-Detection | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.211 ms | 0 - 49 MB | NPU
|
| 106 |
+
| Facial-Attribute-Detection | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.63 ms | 0 - 105 MB | NPU
|
| 107 |
+
| Facial-Attribute-Detection | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 4.258 ms | 0 - 75 MB | NPU
|
| 108 |
+
| Facial-Attribute-Detection | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.813 ms | 0 - 9 MB | NPU
|
| 109 |
+
| Facial-Attribute-Detection | TFLITE | float | Qualcomm® SA8775P | 1.372 ms | 0 - 77 MB | NPU
|
| 110 |
+
| Facial-Attribute-Detection | TFLITE | float | Qualcomm® QCS9075 | 1.194 ms | 0 - 24 MB | NPU
|
| 111 |
+
| Facial-Attribute-Detection | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1.327 ms | 0 - 99 MB | NPU
|
| 112 |
+
| Facial-Attribute-Detection | TFLITE | float | Qualcomm® SA7255P | 4.258 ms | 0 - 75 MB | NPU
|
| 113 |
+
| Facial-Attribute-Detection | TFLITE | float | Qualcomm® SA8295P | 1.507 ms | 0 - 63 MB | NPU
|
| 114 |
+
| Facial-Attribute-Detection | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.493 ms | 0 - 65 MB | NPU
|
| 115 |
+
| Facial-Attribute-Detection | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.417 ms | 0 - 79 MB | NPU
|
| 116 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.284 ms | 0 - 63 MB | NPU
|
| 117 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Qualcomm® QCS6490 | 1.296 ms | 0 - 13 MB | NPU
|
| 118 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.102 ms | 0 - 49 MB | NPU
|
| 119 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.376 ms | 0 - 2 MB | NPU
|
| 120 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Qualcomm® SA8775P | 0.586 ms | 0 - 51 MB | NPU
|
| 121 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.487 ms | 0 - 13 MB | NPU
|
| 122 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Qualcomm® QCM6690 | 2.962 ms | 0 - 42 MB | NPU
|
| 123 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.63 ms | 0 - 65 MB | NPU
|
| 124 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Qualcomm® SA7255P | 1.102 ms | 0 - 49 MB | NPU
|
| 125 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Qualcomm® SA8295P | 0.836 ms | 0 - 43 MB | NPU
|
| 126 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.241 ms | 0 - 42 MB | NPU
|
| 127 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.496 ms | 0 - 43 MB | NPU
|
| 128 |
+
| Facial-Attribute-Detection | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.212 ms | 0 - 53 MB | NPU
|
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| 129 |
|
| 130 |
## License
|
| 131 |
* The license for the original implementation of Facial-Attribute-Detection can be found
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|
| 133 |
|
| 134 |
|
| 135 |
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| 136 |
## Community
|
| 137 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 138 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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tool-versions.yaml
DELETED
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@@ -1,4 +0,0 @@
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|
| 1 |
-
tool_versions:
|
| 2 |
-
onnx:
|
| 3 |
-
qairt: 2.37.1.250807093845_124904
|
| 4 |
-
onnx_runtime: 1.23.0
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