Yolo-v7-Quantized: Optimized for Mobile Deployment
Quantized 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 post-training quantized to int8 using samples from the COCO dataset.
This model is an implementation of Yolo-v7-Quantized found here.
More details on model performance across various devices, can be found here.
Model Details
- Model Type: Object detection
- Model Stats:
- Model checkpoint: YoloV7 Tiny
- Input resolution: 640x640
- Number of parameters: 6.24M
- Model size: 6.23 MB
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|---|
| Yolo-v7-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 2.47 ms | 0 - 29 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 6.293 ms | 0 - 23 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 1.679 ms | 0 - 32 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 4.273 ms | 2 - 68 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 1.639 ms | 0 - 33 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 3.733 ms | 1 - 57 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | SA7255P ADP | SA7255P | TFLITE | 16.581 ms | 0 - 20 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 2.472 ms | 0 - 29 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | SA8295P ADP | SA8295P | TFLITE | 3.839 ms | 0 - 27 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 2.473 ms | 0 - 30 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | SA8775P ADP | SA8775P | TFLITE | 3.49 ms | 0 - 22 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | TFLITE | 10.072 ms | 0 - 31 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | RB5 (Proxy) | QCS8250 Proxy | TFLITE | 58.745 ms | 15 - 53 MB | INT8 | GPU | -- |
| Yolo-v7-Quantized | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 16.581 ms | 0 - 20 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 2.471 ms | 0 - 31 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 3.49 ms | 0 - 22 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 3.055 ms | 0 - 42 MB | INT8 | NPU | -- |
| Yolo-v7-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.136 ms | 5 - 5 MB | INT8 | NPU | -- |
License
- The license for the original implementation of Yolo-v7-Quantized can be found here.
- The license for the compiled assets for on-device deployment can be found here
References
- YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
- Source Model Implementation
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.