YOLOv8-Detection-Quantized: Optimized for Mobile Deployment
Quantized real-time object detection optimized for mobile and edge by Ultralytics
Ultralytics YOLOv8 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 YOLOv8-Detection-Quantized found here.
More details on model performance accross various devices, can be found here.
Model Details
- Model Type: Object detection
- Model Stats:
- Model checkpoint: YOLOv8-N
- Input resolution: 640x640
- Number of parameters: 3.18M
- Model size: 3.26 MB
| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|---|
| YOLOv8-Detection-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 1.915 ms | 0 - 1 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 2.265 ms | 0 - 9 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 5.638 ms | 7 - 11 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 1.273 ms | 0 - 57 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1.489 ms | 1 - 31 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 3.976 ms | 3 - 151 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | TFLITE | 4.734 ms | 0 - 42 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | QNN | 5.781 ms | 1 - 8 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | RB5 (Proxy) | QCS8250 Proxy | TFLITE | 46.023 ms | 3 - 16 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 1.891 ms | 0 - 6 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 1.941 ms | 1 - 2 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 1.897 ms | 0 - 9 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | SA8255 (Proxy) | SA8255P Proxy | QNN | 1.97 ms | 1 - 2 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 1.919 ms | 0 - 2 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | SA8775 (Proxy) | SA8775P Proxy | QNN | 1.953 ms | 5 - 6 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 1.919 ms | 0 - 3 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | SA8650 (Proxy) | SA8650P Proxy | QNN | 1.979 ms | 1 - 2 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 2.091 ms | 0 - 57 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 2.512 ms | 1 - 33 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 1.206 ms | 0 - 38 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 1.487 ms | 1 - 27 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 3.993 ms | 6 - 122 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 2.263 ms | 1 - 1 MB | INT8 | NPU | -- |
| YOLOv8-Detection-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.306 ms | 7 - 7 MB | INT8 | NPU | -- |
License
- The license for the original implementation of YOLOv8-Detection-Quantized 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 API (serverless) does not yet support pytorch models for this pipeline type.