--- library_name: pytorch license: other tags: - real_time - android pipeline_tag: object-detection --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolov5/web-assets/model_demo.png) # Yolo-v5: Optimized for Mobile Deployment ## Real-time object detection optimized for mobile and edge YoloV5 is a machine learning model that predicts bounding boxes and classes of objects in an image. This model is an implementation of Yolo-v5 found [here](https://github.com/ultralytics/yolov5). This repository provides scripts to run Yolo-v5 on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/yolov5). **WARNING**: The model assets are not readily available for download due to licensing restrictions. ### Model Details - **Model Type:** Model_use_case.object_detection - **Model Stats:** - Model checkpoint: YoloV5-M - Input resolution: 640x640 - Number of parameters: 21.2M - Model size (float): 81.1 MB - Model size (w8a16): 21.8 MB | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | Yolo-v5 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 64.064 ms | 0 - 78 MB | NPU | -- | | Yolo-v5 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 64.07 ms | 4 - 82 MB | NPU | -- | | Yolo-v5 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 23.633 ms | 0 - 98 MB | NPU | -- | | Yolo-v5 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 26.405 ms | 5 - 57 MB | NPU | -- | | Yolo-v5 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 11.268 ms | 0 - 19 MB | NPU | -- | | Yolo-v5 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 10.941 ms | 5 - 55 MB | NPU | -- | | Yolo-v5 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 13.033 ms | 0 - 83 MB | NPU | -- | | Yolo-v5 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 80.283 ms | 0 - 78 MB | NPU | -- | | Yolo-v5 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 17.549 ms | 3 - 84 MB | NPU | -- | | Yolo-v5 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 8.677 ms | 0 - 111 MB | NPU | -- | | Yolo-v5 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 8.705 ms | 5 - 115 MB | NPU | -- | | Yolo-v5 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 9.618 ms | 5 - 84 MB | NPU | -- | | Yolo-v5 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 6.814 ms | 0 - 80 MB | NPU | -- | | Yolo-v5 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 6.248 ms | 5 - 89 MB | NPU | -- | | Yolo-v5 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 7.4 ms | 3 - 84 MB | NPU | -- | | Yolo-v5 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | TFLITE | 4.951 ms | 0 - 78 MB | NPU | -- | | Yolo-v5 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_DLC | 4.5 ms | 5 - 109 MB | NPU | -- | | Yolo-v5 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | ONNX | 5.894 ms | 5 - 72 MB | NPU | -- | | Yolo-v5 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 11.567 ms | 66 - 66 MB | NPU | -- | | Yolo-v5 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 13.399 ms | 46 - 46 MB | NPU | -- | | Yolo-v5 | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 21.025 ms | 2 - 73 MB | NPU | -- | | Yolo-v5 | w8a16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 13.096 ms | 2 - 87 MB | NPU | -- | | Yolo-v5 | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 8.847 ms | 2 - 24 MB | NPU | -- | | Yolo-v5 | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 12.378 ms | 0 - 78 MB | NPU | -- | | Yolo-v5 | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 9.044 ms | 1 - 72 MB | NPU | -- | | Yolo-v5 | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 48.488 ms | 2 - 99 MB | NPU | -- | | Yolo-v5 | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | ONNX | 821.39 ms | 172 - 190 MB | CPU | -- | | Yolo-v5 | w8a16 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | ONNX | 754.241 ms | 161 - 173 MB | CPU | -- | | Yolo-v5 | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 5.88 ms | 2 - 90 MB | NPU | -- | | Yolo-v5 | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 8.593 ms | 1 - 123 MB | NPU | -- | | Yolo-v5 | w8a16 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 4.25 ms | 0 - 74 MB | NPU | -- | | Yolo-v5 | w8a16 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 5.289 ms | 1 - 101 MB | NPU | -- | | Yolo-v5 | w8a16 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_DLC | 3.789 ms | 2 - 78 MB | NPU | -- | | Yolo-v5 | w8a16 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | ONNX | 3.986 ms | 1 - 96 MB | NPU | -- | | Yolo-v5 | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 9.541 ms | 45 - 45 MB | NPU | -- | | Yolo-v5 | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 11.914 ms | 24 - 24 MB | NPU | -- | ## Installation Install the package via pip: ```bash pip install "qai-hub-models[yolov5]" ``` ## 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.yolov5.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.yolov5.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.yolov5.export ``` ## How does this work? This [export script](https://aihub.qualcomm.com/models/yolov5/qai_hub_models/models/Yolo-v5/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.yolov5 import Model # Load the model torch_model = Model.from_pretrained() # Device device = hub.Device("Samsung Galaxy S25") # 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 access](https://myaccount.qualcomm.com/signup). ## Run demo on a cloud-hosted device You can also run the demo on-device. ```bash python -m qai_hub_models.models.yolov5.demo --eval-mode 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.yolov5.demo -- --eval-mode 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-v5's performance across various devices [here](https://aihub.qualcomm.com/models/yolov5). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of Yolo-v5 can be found [here](https://github.com/ultralytics/yolov5?tab=AGPL-3.0-1-ov-file#readme). * The license for the compiled assets for on-device deployment can be found [here](https://github.com/ultralytics/yolov5?tab=AGPL-3.0-1-ov-file#readme) ## References * [Source Model Implementation](https://github.com/ultralytics/yolov5) ## 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).