--- library_name: pytorch license: gpl-3.0 pipeline_tag: object-detection tags: - real_time - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolov6/web-assets/banner.png) # Yolo-v6: Optimized for Mobile Deployment ## Real-time object detection optimized for mobile and edge YoloV6 is a machine learning model that predicts bounding boxes and classes of objects in an image. This model is an implementation of Yolo-v6 found [here](https://github.com/meituan/YOLOv6/). This repository provides scripts to run Yolo-v6 on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/yolov6). ### Model Details - **Model Type:** Object detection - **Model Stats:** - Model checkpoint: YoloV6-N - Input resolution: 640x640 - Number of parameters: 4.68M - Model size: 17.9 MB | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model | ---|---|---|---|---|---|---|---| | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 7.848 ms | 0 - 7 MB | FP16 | NPU | [Yolo-v6.tflite](https://huggingface.co/qualcomm/Yolo-v6/blob/main/Yolo-v6.tflite) | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 7.283 ms | 5 - 17 MB | FP16 | NPU | [Yolo-v6.so](https://huggingface.co/qualcomm/Yolo-v6/blob/main/Yolo-v6.so) ## Installation This model can be installed as a Python package via pip. ```bash pip install qai-hub-models ``` ## 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.yolov6.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.yolov6.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.yolov6.export ``` ``` Profile Job summary of Yolo-v6 -------------------------------------------------- Device: Samsung Galaxy S23 Ultra (13) Estimated Inference Time: 7.85 ms Estimated Peak Memory Range: 0.03-6.90 MB Compute Units: NPU (182) | Total (182) Profile Job summary of Yolo-v6 -------------------------------------------------- Device: Samsung Galaxy S23 Ultra (13) Estimated Inference Time: 7.28 ms Estimated Peak Memory Range: 4.70-16.65 MB Compute Units: NPU (230) | Total (230) ``` ## How does this work? This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/Yolo-v6/export.py) leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) 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. ```python import torch import qai_hub as hub from qai_hub_models.models.yolov6 import Model # Load the model torch_model = Model.from_pretrained() torch_model.eval() # Device device = hub.Device("Samsung Galaxy S23") # 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. ```python 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. ```python 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 early access](https://aihub.qualcomm.com/sign-up). ## Run demo on a cloud-hosted device You can also run the demo on-device. ```bash python -m qai_hub_models.models.yolov6.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.yolov6.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 Yolo-v6's performance across various devices [here](https://aihub.qualcomm.com/models/yolov6). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License - The license for the original implementation of Yolo-v6 can be found [here](https://github.com/meituan/YOLOv6/blob/47625514e7480706a46ff3c0cd0252907ac12f22/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 * [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://arxiv.org/abs/2209.02976) * [Source Model Implementation](https://github.com/meituan/YOLOv6/) ## Community * Join [our AI Hub Slack community](https://join.slack.com/t/qualcomm-ai-hub/shared_invite/zt-2dgf95loi-CXHTDRR1rvPgQWPO~ZZZJg) 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).