README.md

---
library_name: pytorch
license: agpl-3.0
tags:
- real_time
- android
pipeline_tag: object-detection

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/yolov3/web-assets/model_demo.png)

# Yolo-v3: Optimized for Mobile Deployment
## Real-time object detection optimized for mobile and edge


YoloV3 is a machine learning model that predicts bounding boxes and classes of objects in an image.

This model is an implementation of Yolo-v3 found [here](https://github.com/ultralytics/yolov3/tree/v8).


 More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/yolov3).

### Model Details

- **Model Type:** Object detection
- **Model Stats:**
  - Model checkpoint: YoloV3 Tiny
  - Input resolution: 416p (416x416)
  - Number of parameters: 8.85M
  - Model size: 24.4 MB

| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| Yolo-v3 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 16.439 ms | 0 - 8 MB | FP16 | NPU | -- |
| Yolo-v3 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 11.205 ms | 5 - 12 MB | FP16 | NPU | -- |
| Yolo-v3 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 14.341 ms | 7 - 76 MB | FP16 | NPU | -- |
| Yolo-v3 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 10.551 ms | 0 - 96 MB | FP16 | NPU | -- |
| Yolo-v3 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 8.143 ms | 5 - 33 MB | FP16 | NPU | -- |
| Yolo-v3 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 10.466 ms | 14 - 47 MB | FP16 | NPU | -- |
| Yolo-v3 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 8.399 ms | 0 - 72 MB | FP16 | NPU | -- |
| Yolo-v3 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 7.772 ms | 5 - 31 MB | FP16 | NPU | -- |
| Yolo-v3 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 11.244 ms | 15 - 42 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 107.772 ms | 0 - 70 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 97.783 ms | 5 - 14 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 16.436 ms | 0 - 7 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 8.441 ms | 5 - 8 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 21.014 ms | 0 - 71 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 11.718 ms | 1 - 11 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 17.995 ms | 0 - 88 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 13.219 ms | 5 - 29 MB | FP16 | NPU | -- |
| Yolo-v3 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 8.602 ms | 5 - 5 MB | FP16 | NPU | -- |
| Yolo-v3 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 14.485 ms | 5 - 5 MB | FP16 | NPU | -- |




## License
* The license for the original implementation of Yolo-v3 can be found
  [here](https://github.com/ultralytics/yolov3/blob/v8/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://github.com/ultralytics/yolov3/blob/v8/LICENSE)



## References
* [YOLOv3: An Incremental Improvement](https://arxiv.org/abs/1804.02767)
* [Source Model Implementation](https://github.com/ultralytics/yolov3/tree/v8)



## Community
* Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:[email protected]).

## Usage and Limitations

Model may not be used for or in connection with any of the following applications:

- Accessing essential private and public services and benefits;
- Administration of justice and democratic processes;
- Assessing or recognizing the emotional state of a person;
- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
- Education and vocational training;
- Employment and workers management;
- Exploitation of the vulnerabilities of persons resulting in harmful behavior;
- General purpose social scoring;
- Law enforcement;
- Management and operation of critical infrastructure;
- Migration, asylum and border control management;
- Predictive policing;
- Real-time remote biometric identification in public spaces;
- Recommender systems of social media platforms;
- Scraping of facial images (from the internet or otherwise); and/or
- Subliminal manipulation