--- library_name: pytorch license: other tags: - real_time - bu_auto - 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 Qualcomm Devices YoloV5 is a machine learning model that predicts bounding boxes and classes of objects in an image. This is based on the implementation of Yolo-v5 found [here](https://github.com/ultralytics/yolov5). 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/yolov5) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary). 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. ## Getting Started Due to licensing restrictions, we cannot distribute pre-exported model assets for this model. Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/yolov5) Python library to compile and export the model with your own: - Custom weights (e.g., fine-tuned checkpoints) - Custom input shapes - Target device and runtime configurations See our repository for [Yolo-v5 on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/yolov5) for usage instructions. ## 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 ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | Yolo-v5 | ONNX | float | Snapdragon® X Elite | 13.554 ms | 46 - 46 MB | NPU | Yolo-v5 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 9.662 ms | 1 - 281 MB | NPU | Yolo-v5 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 12.862 ms | 0 - 54 MB | NPU | Yolo-v5 | ONNX | float | Qualcomm® QCS9075 | 22.415 ms | 5 - 12 MB | NPU | Yolo-v5 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.321 ms | 2 - 233 MB | NPU | Yolo-v5 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.291 ms | 5 - 216 MB | NPU | Yolo-v5 | ONNX | float | Snapdragon® X2 Elite | 11.001 ms | 46 - 46 MB | NPU | Yolo-v5 | ONNX | w8a16 | Snapdragon® X Elite | 10.287 ms | 23 - 23 MB | NPU | Yolo-v5 | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 6.166 ms | 3 - 311 MB | NPU | Yolo-v5 | ONNX | w8a16 | Qualcomm® QCS6490 | 1704.695 ms | 93 - 96 MB | CPU | Yolo-v5 | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 9.591 ms | 0 - 29 MB | NPU | Yolo-v5 | ONNX | w8a16 | Qualcomm® QCS9075 | 12.731 ms | 2 - 5 MB | NPU | Yolo-v5 | ONNX | w8a16 | Qualcomm® QCM6690 | 871.082 ms | 84 - 94 MB | CPU | Yolo-v5 | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 5.372 ms | 0 - 259 MB | NPU | Yolo-v5 | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 834.105 ms | 100 - 111 MB | CPU | Yolo-v5 | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 4.08 ms | 3 - 270 MB | NPU | Yolo-v5 | ONNX | w8a16 | Snapdragon® X2 Elite | 4.9 ms | 24 - 24 MB | NPU | Yolo-v5 | QNN_DLC | float | Snapdragon® X Elite | 11.926 ms | 5 - 5 MB | NPU | Yolo-v5 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 8.467 ms | 0 - 235 MB | NPU | Yolo-v5 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 63.683 ms | 1 - 189 MB | NPU | Yolo-v5 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 11.641 ms | 5 - 6 MB | NPU | Yolo-v5 | QNN_DLC | float | Qualcomm® QCS9075 | 17.796 ms | 5 - 11 MB | NPU | Yolo-v5 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 25.748 ms | 5 - 261 MB | NPU | Yolo-v5 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 6.374 ms | 0 - 197 MB | NPU | Yolo-v5 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.777 ms | 5 - 208 MB | NPU | Yolo-v5 | QNN_DLC | float | Snapdragon® X2 Elite | 6.704 ms | 5 - 5 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® X Elite | 9.542 ms | 2 - 2 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 5.854 ms | 2 - 286 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 31.999 ms | 4 - 8 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 21.058 ms | 1 - 232 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 8.821 ms | 2 - 4 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 10.973 ms | 2 - 6 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 98.601 ms | 2 - 268 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 13.484 ms | 2 - 285 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 4.29 ms | 2 - 238 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 13.047 ms | 2 - 251 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 3.215 ms | 2 - 246 MB | NPU | Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 4.261 ms | 2 - 2 MB | NPU | Yolo-v5 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 8.196 ms | 0 - 290 MB | NPU | Yolo-v5 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 63.131 ms | 0 - 222 MB | NPU | Yolo-v5 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 10.363 ms | 0 - 3 MB | NPU | Yolo-v5 | TFLITE | float | Qualcomm® QCS9075 | 17.997 ms | 0 - 57 MB | NPU | Yolo-v5 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 30.628 ms | 0 - 315 MB | NPU | Yolo-v5 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 6.078 ms | 0 - 227 MB | NPU | Yolo-v5 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.43 ms | 0 - 235 MB | NPU ## 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). ## 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).