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.
More details on model performance across various devices, can be found here.
Model Details
- Model Type: Object detection
- Model Stats:
- Model checkpoint: YoloV5-M
- Input resolution: 640x640
- Number of parameters: 21.2M
- Model size: 81.1 MB
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|---|
| Yolo-v5 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 23.747 ms | 6 - 38 MB | FP16 | NPU | -- |
| Yolo-v5 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 21.883 ms | 6 - 8 MB | FP16 | NPU | -- |
| Yolo-v5 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 27.816 ms | 1 - 119 MB | FP16 | NPU | -- |
| Yolo-v5 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 18.188 ms | 5 - 104 MB | FP16 | NPU | -- |
| Yolo-v5 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 16.767 ms | 5 - 25 MB | FP16 | NPU | -- |
| Yolo-v5 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 21.418 ms | 7 - 134 MB | FP16 | NPU | -- |
| Yolo-v5 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 16.687 ms | 5 - 82 MB | FP16 | NPU | -- |
| Yolo-v5 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 15.715 ms | 5 - 128 MB | FP16 | NPU | -- |
| Yolo-v5 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 20.519 ms | 5 - 120 MB | FP16 | NPU | -- |
| Yolo-v5 | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 370.263 ms | 1 - 74 MB | FP16 | NPU | -- |
| Yolo-v5 | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 364.102 ms | 1 - 10 MB | FP16 | NPU | -- |
| Yolo-v5 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 23.754 ms | 6 - 39 MB | FP16 | NPU | -- |
| Yolo-v5 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 22.141 ms | 5 - 7 MB | FP16 | NPU | -- |
| Yolo-v5 | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 34.771 ms | 0 - 74 MB | FP16 | NPU | -- |
| Yolo-v5 | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 30.88 ms | 1 - 11 MB | FP16 | NPU | -- |
| Yolo-v5 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 34.864 ms | 6 - 87 MB | FP16 | NPU | -- |
| Yolo-v5 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 42.293 ms | 5 - 44 MB | FP16 | NPU | -- |
| Yolo-v5 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 21.469 ms | 5 - 5 MB | FP16 | NPU | -- |
| Yolo-v5 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 28.022 ms | 39 - 39 MB | FP16 | NPU | -- |
License
- The license for the original implementation of Yolo-v5 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
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The HF Inference API does not support object-detection models for pytorch library.