YOLOv10b is the large-scale model in the YOLOv10 family, designed for high-precision object detection tasks. Compared to the lightweight and medium variants, YOLOv10b features a deeper network architecture and more parameters, enabling it to capture richer feature representations and significantly improve detection of small objects and complex scenes. The model employs an advanced anchor-free mechanism, combined with multi-scale feature fusion and a powerful decoupled head design, enhancing detection accuracy and robustness. YOLOv10b is suitable for deployment on high-performance servers or advanced edge devices, widely used in autonomous driving, intelligent security, and industrial inspection applications with demanding requirements.

Source model

• Input shape: 1x3x640x640
• Number of parameters: 19.62M
• Model size: 72.99M
• Output shape: 1x300x6

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 APLUX 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