YOLO11n-obb is a lightweight oriented bounding box (OBB) version of the YOLO series, designed for real-time detection of direction-sensitive objects in resource-constrained environments.

• Architecture: Utilizes an anchor-free backbone with an orientation-aware detection head to identify rotated objects such as vehicles, text regions, and drones.
• Orientation Detection: Predicts rotation angles alongside bounding boxes, enabling accurate detection of inclined targets.
• Efficient Inference: Maintains a low parameter count and computational footprint, offering near real-time performance suitable for mobile and embedded platforms.
• Use Cases: Ideal for drone-based aircraft detection, industrial part localization, and map interpretation tasks involving rotated buildings or traffic signs.

Source model

• Input shape: 1x3x1024x1024
• Number of parameters: 2.56M
• Model size: 10.32M
• Output shape: 1x20x21504

Source model repository: yolo11n-obb

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