metadata
library_name: pytorch
license: agpl-3.0
tags:
- real_time
- android
pipeline_tag: object-detection
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.
More details on model performance across various devices, can be found here.
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 | 16.25 ms | 0 - 8 MB | FP16 | NPU | -- |
| Yolo-v3 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 8.663 ms | 5 - 7 MB | FP16 | NPU | -- |
| Yolo-v3 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 12.159 ms | 6 - 72 MB | FP16 | NPU | -- |
| Yolo-v3 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 10.704 ms | 0 - 95 MB | FP16 | NPU | -- |
| Yolo-v3 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 6.163 ms | 5 - 23 MB | FP16 | NPU | -- |
| Yolo-v3 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 8.425 ms | 16 - 45 MB | FP16 | NPU | -- |
| Yolo-v3 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 8.353 ms | 0 - 73 MB | FP16 | NPU | -- |
| Yolo-v3 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 7.857 ms | 5 - 30 MB | FP16 | NPU | -- |
| Yolo-v3 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 11.015 ms | 5 - 27 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 107.671 ms | 0 - 69 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 97.988 ms | 0 - 10 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 16.78 ms | 0 - 12 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 8.348 ms | 5 - 8 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 21.365 ms | 0 - 71 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 11.777 ms | 1 - 10 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 18.119 ms | 0 - 88 MB | FP16 | NPU | -- |
| Yolo-v3 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 12.984 ms | 5 - 29 MB | FP16 | NPU | -- |
| Yolo-v3 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 8.534 ms | 5 - 5 MB | FP16 | NPU | -- |
| Yolo-v3 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 14.306 ms | 5 - 5 MB | FP16 | NPU | -- |
License
- The license for the original implementation of Yolo-v3 can be found here.
- The license for the compiled assets for on-device deployment can be found here
References
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.
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