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---
library_name: pytorch
license: gpl-3.0
tags:
- real_time
- android
pipeline_tag: object-detection
---
# Yolo-v7: Optimized for Mobile Deployment
## Real-time object detection optimized for mobile and edge
YoloV7 is a machine learning model that predicts bounding boxes and classes of objects in an image.
This model is an implementation of Yolo-v7 found [here](https://github.com/WongKinYiu/yolov7/).
More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/yolov7).
### Model Details
- **Model Type:** Object detection
- **Model Stats:**
- Model checkpoint: YoloV7 Tiny
- Input resolution: 640x640
- Number of parameters: 6.39M
- Model size: 24.4 MB
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| Yolo-v7 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 17.62 ms | 1 - 44 MB | FP16 | NPU | -- |
| Yolo-v7 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 13.89 ms | 2 - 46 MB | FP16 | NPU | -- |
| Yolo-v7 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 12.802 ms | 1 - 37 MB | FP16 | NPU | -- |
| Yolo-v7 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 6.063 ms | 5 - 23 MB | FP16 | NPU | -- |
| Yolo-v7 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 10.083 ms | 6 - 65 MB | FP16 | NPU | -- |
| Yolo-v7 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 10.755 ms | 0 - 33 MB | FP16 | NPU | -- |
| Yolo-v7 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 5.897 ms | 5 - 50 MB | FP16 | NPU | -- |
| Yolo-v7 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 9.454 ms | 5 - 51 MB | FP16 | NPU | -- |
| Yolo-v7 | SA7255P ADP | SA7255P | TFLITE | 109.65 ms | 1 - 23 MB | FP16 | NPU | -- |
| Yolo-v7 | SA7255P ADP | SA7255P | QNN | 98.564 ms | 0 - 8 MB | FP16 | NPU | -- |
| Yolo-v7 | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 17.508 ms | 1 - 15 MB | FP16 | NPU | -- |
| Yolo-v7 | SA8255 (Proxy) | SA8255P Proxy | QNN | 8.719 ms | 5 - 8 MB | FP16 | NPU | -- |
| Yolo-v7 | SA8295P ADP | SA8295P | TFLITE | 22.193 ms | 1 - 30 MB | FP16 | NPU | -- |
| Yolo-v7 | SA8295P ADP | SA8295P | QNN | 13.676 ms | 0 - 15 MB | FP16 | NPU | -- |
| Yolo-v7 | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 17.609 ms | 1 - 14 MB | FP16 | NPU | -- |
| Yolo-v7 | SA8650 (Proxy) | SA8650P Proxy | QNN | 8.692 ms | 5 - 7 MB | FP16 | NPU | -- |
| Yolo-v7 | SA8775P ADP | SA8775P | TFLITE | 22.185 ms | 0 - 24 MB | FP16 | NPU | -- |
| Yolo-v7 | SA8775P ADP | SA8775P | QNN | 13.053 ms | 1 - 10 MB | FP16 | NPU | -- |
| Yolo-v7 | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 109.65 ms | 1 - 23 MB | FP16 | NPU | -- |
| Yolo-v7 | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 98.564 ms | 0 - 8 MB | FP16 | NPU | -- |
| Yolo-v7 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 17.515 ms | 0 - 13 MB | FP16 | NPU | -- |
| Yolo-v7 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 8.697 ms | 5 - 7 MB | FP16 | NPU | -- |
| Yolo-v7 | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 22.185 ms | 0 - 24 MB | FP16 | NPU | -- |
| Yolo-v7 | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 13.053 ms | 1 - 10 MB | FP16 | NPU | -- |
| Yolo-v7 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 20.436 ms | 1 - 38 MB | FP16 | NPU | -- |
| Yolo-v7 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 10.483 ms | 5 - 36 MB | FP16 | NPU | -- |
| Yolo-v7 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 9.44 ms | 5 - 5 MB | FP16 | NPU | -- |
| Yolo-v7 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 13.944 ms | 9 - 9 MB | FP16 | NPU | -- |
## License
* The license for the original implementation of Yolo-v7 can be found
[here](https://github.com/WongKinYiu/yolov7/blob/main/LICENSE.md).
* The license for the compiled assets for on-device deployment can be found [here](https://github.com/WongKinYiu/yolov7/blob/main/LICENSE.md)
## References
* [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
* [Source Model Implementation](https://github.com/WongKinYiu/yolov7/)
## 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
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