YOLO11 builds on YOLOv9 and YOLOv10, incorporating improved model structure design, enhanced feature extraction techniques, and optimized training methods. What really makes YOLO11 stand out is its impressive combination of speed, accuracy, and efficiency, making it one of the most powerful models Ultralytics has created to date. Through improved design, YOLO11 provides better feature extraction, which is the process of identifying important patterns and details from images, and can more accurately capture complex aspects even in challenging scenes.

Source model

• Input shape: 640x640
• Number of parameters: 2.50M
• Model size: 10.21M
• Output shape: 1x84x8400

Source model repository: YOLOv11n

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