YOLOv6 is an advanced real-time object detection model based on the "You Only Look Once" framework. It achieves faster inference speeds while maintaining high accuracy, making it suitable for various edge devices and high-performance servers. YOLOv6 enhances its ability to detect small objects and improves the model's generalization performance by optimizing the network architecture and introducing new loss functions. Additionally, YOLOv6 supports multi-scale training, ensuring excellent performance across different resolutions. It is widely applied in areas such as video surveillance, autonomous driving, and intelligent security.
Source model
- Input shape: 1x3x1280x1280
- Number of parameters: 39.47M
- Model size: 158.5MB
- Output shape: 1x34000x85
The source model can be found here
When the user has fine-tuned the source model, the model conversion process must be performed again.
Users can refer to either of the following two methods to complete the model conversion:
Using AIMO for model conversion: Click Model Conversion Reference in the Performance Reference section on the right to view the conversion steps.
Using Qualcomm QNN for model conversion: Please refer to the Qualcomm QNN Documentation.
The model performance benchmarks and example code provided by Model Farm are all implemented based on the AIDLux AidLite SDK.
For models in .bin format, you can use either of the following two inference engines to run inference on Qualcomm chips:
Inference using APLUX AidLite: please refer to the APLUX AidLite Developer Documentation
Inference using Qualcomm QNN: Please refer to the Qualcomm QNN Documentation
Inference Example Code
The inference example code is implemented using the AidLite SDK.
Click Model & Code to download the model files and the inference code package. The file structure is as follows:
/model_farm_{model_name}_aidlite
|__ models # folder where model files are stored
|__ python # aidlite python model inference example
|__ cpp # aidlite cpp model inference example
|__ README.md