README.md

---
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 | 13.392 ms | 46 - 46 MB | NPU
| Yolo-v5 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 9.711 ms | 1 - 218 MB | NPU
| Yolo-v5 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 12.922 ms | 0 - 54 MB | NPU
| Yolo-v5 | ONNX | float | Qualcomm® QCS9075 | 21.799 ms | 5 - 12 MB | NPU
| Yolo-v5 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.358 ms | 0 - 163 MB | NPU
| Yolo-v5 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.934 ms | 2 - 156 MB | NPU
| Yolo-v5 | ONNX | w8a16 | Snapdragon® X Elite | 11.535 ms | 24 - 24 MB | NPU
| Yolo-v5 | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 7.7 ms | 3 - 253 MB | NPU
| Yolo-v5 | ONNX | w8a16 | Qualcomm® QCS6490 | 1734.938 ms | 93 - 103 MB | CPU
| Yolo-v5 | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 11.586 ms | 0 - 28 MB | NPU
| Yolo-v5 | ONNX | w8a16 | Qualcomm® QCS9075 | 12.749 ms | 2 - 5 MB | NPU
| Yolo-v5 | ONNX | w8a16 | Qualcomm® QCM6690 | 844.595 ms | 97 - 106 MB | CPU
| Yolo-v5 | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 5.083 ms | 0 - 201 MB | NPU
| Yolo-v5 | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 817.456 ms | 97 - 108 MB | CPU
| Yolo-v5 | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 3.928 ms | 3 - 209 MB | NPU
| Yolo-v5 | QNN_DLC | float | Snapdragon® X Elite | 11.954 ms | 5 - 5 MB | NPU
| Yolo-v5 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 8.492 ms | 0 - 273 MB | NPU
| Yolo-v5 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 63.73 ms | 1 - 214 MB | NPU
| Yolo-v5 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 11.34 ms | 5 - 7 MB | NPU
| Yolo-v5 | QNN_DLC | float | Qualcomm® QCS9075 | 18.467 ms | 5 - 11 MB | NPU
| Yolo-v5 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 26.11 ms | 5 - 263 MB | NPU
| Yolo-v5 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 6.344 ms | 0 - 214 MB | NPU
| Yolo-v5 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.773 ms | 5 - 214 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® X Elite | 9.458 ms | 2 - 2 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 5.885 ms | 2 - 285 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 32.048 ms | 1 - 5 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 20.959 ms | 1 - 232 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 8.753 ms | 2 - 4 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 10.908 ms | 2 - 6 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 124.027 ms | 2 - 269 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 13.553 ms | 2 - 285 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 4.192 ms | 2 - 239 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 12.722 ms | 2 - 251 MB | NPU
| Yolo-v5 | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 3.234 ms | 2 - 246 MB | NPU
| Yolo-v5 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 8.197 ms | 0 - 315 MB | NPU
| Yolo-v5 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 63.071 ms | 0 - 236 MB | NPU
| Yolo-v5 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 10.819 ms | 0 - 3 MB | NPU
| Yolo-v5 | TFLITE | float | Qualcomm® QCS9075 | 17.898 ms | 0 - 57 MB | NPU
| Yolo-v5 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 30.761 ms | 0 - 314 MB | NPU
| Yolo-v5 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 6.127 ms | 0 - 240 MB | NPU
| Yolo-v5 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.317 ms | 0 - 238 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).