--- library_name: pytorch license: bsd-3-clause pipeline_tag: object-detection tags: - real_time - quantized - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/gear_guard_net_quantized/web-assets/model_demo.png) # PPE-Detection-Quantized: Optimized for Mobile Deployment ## Object detection for personal protective equipments (PPE) with quantized model Detect if a person is wearing personal protective equipments (PPE) in real-time. This model is an implementation of PPE-Detection-Quantized found [here](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/gear_guard_net_quantized/model.py). This repository provides scripts to run PPE-Detection-Quantized on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/gear_guard_net_quantized). ### Model Details - **Model Type:** Object detection - **Model Stats:** - Inference latency: RealTime - Input resolution: 320x192 - Number of parameters: 7.02M - Model size: 6.7 MB - Number of output classes: 2 | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | PPE-Detection-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 0.253 ms | 0 - 24 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 0.308 ms | 0 - 24 MB | INT8 | NPU | [PPE-Detection-Quantized.so](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.so) | | PPE-Detection-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 0.607 ms | 0 - 23 MB | INT8 | NPU | [PPE-Detection-Quantized.onnx](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.onnx) | | PPE-Detection-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.185 ms | 0 - 27 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.228 ms | 0 - 29 MB | INT8 | NPU | [PPE-Detection-Quantized.so](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.so) | | PPE-Detection-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 0.434 ms | 0 - 25 MB | INT8 | NPU | [PPE-Detection-Quantized.onnx](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.onnx) | | PPE-Detection-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.183 ms | 0 - 20 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.229 ms | 0 - 20 MB | INT8 | NPU | Use Export Script | | PPE-Detection-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.403 ms | 0 - 21 MB | INT8 | NPU | [PPE-Detection-Quantized.onnx](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.onnx) | | PPE-Detection-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | TFLITE | 1.23 ms | 0 - 19 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | QNN | 1.792 ms | 0 - 11 MB | INT8 | NPU | Use Export Script | | PPE-Detection-Quantized | RB5 (Proxy) | QCS8250 Proxy | TFLITE | 5.523 ms | 0 - 12 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.249 ms | 0 - 14 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 0.312 ms | 0 - 4 MB | INT8 | NPU | Use Export Script | | PPE-Detection-Quantized | SA7255P ADP | SA7255P | TFLITE | 3.827 ms | 0 - 13 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | SA7255P ADP | SA7255P | QNN | 4.181 ms | 0 - 10 MB | INT8 | NPU | Use Export Script | | PPE-Detection-Quantized | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.251 ms | 0 - 24 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | SA8255 (Proxy) | SA8255P Proxy | QNN | 0.307 ms | 0 - 3 MB | INT8 | NPU | Use Export Script | | PPE-Detection-Quantized | SA8295P ADP | SA8295P | TFLITE | 0.713 ms | 0 - 19 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | SA8295P ADP | SA8295P | QNN | 1.068 ms | 0 - 14 MB | INT8 | NPU | Use Export Script | | PPE-Detection-Quantized | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 0.251 ms | 0 - 24 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | SA8650 (Proxy) | SA8650P Proxy | QNN | 0.33 ms | 0 - 3 MB | INT8 | NPU | Use Export Script | | PPE-Detection-Quantized | SA8775P ADP | SA8775P | TFLITE | 0.556 ms | 0 - 14 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | SA8775P ADP | SA8775P | QNN | 0.76 ms | 0 - 10 MB | INT8 | NPU | Use Export Script | | PPE-Detection-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 0.392 ms | 0 - 20 MB | INT8 | NPU | [PPE-Detection-Quantized.tflite](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.tflite) | | PPE-Detection-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 0.458 ms | 0 - 26 MB | INT8 | NPU | Use Export Script | | PPE-Detection-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 0.407 ms | 0 - 0 MB | INT8 | NPU | Use Export Script | | PPE-Detection-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 0.629 ms | 8 - 8 MB | INT8 | NPU | [PPE-Detection-Quantized.onnx](https://huggingface.co/qualcomm/PPE-Detection-Quantized/blob/main/PPE-Detection-Quantized.onnx) | ## Installation This model can be installed as a Python package via pip. ```bash pip install "qai-hub-models[gear_guard_net_quantized]" ``` ## 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.gear_guard_net_quantized.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.gear_guard_net_quantized.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.gear_guard_net_quantized.export ``` ``` Profiling Results ------------------------------------------------------------ PPE-Detection-Quantized Device : Samsung Galaxy S23 (13) Runtime : TFLITE Estimated inference time (ms) : 0.3 Estimated peak memory usage (MB): [0, 24] Total # Ops : 86 Compute Unit(s) : NPU (86 ops) ``` ## Run demo on a cloud-hosted device You can also run the demo on-device. ```bash python -m qai_hub_models.models.gear_guard_net_quantized.demo --on-device ``` **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.gear_guard_net_quantized.demo -- --on-device ``` ## 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 PPE-Detection-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/gear_guard_net_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of PPE-Detection-Quantized can be found [here](https://github.com/qcom-ai-hub/ai-hub-models-internal/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 * [None](None) * [Source Model Implementation](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/gear_guard_net_quantized/model.py) ## 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:ai-hub-support@qti.qualcomm.com).