YOLOv13-L is the large variant of the 13th-generation YOLO series, designed for scenarios that demand high accuracy and robustness. Compared to YOLOv13-M, YOLOv13-L features deeper layers, wider channels, and improved multi-scale feature fusion along with global attention mechanisms—significantly enhancing its performance in complex backgrounds, dense multi-object scenes, and small-object detection.

The model achieves higher mean Average Precision (mAP) on benchmarks such as COCO and Objects365, making it well-suited for applications including autonomous driving, smart city surveillance, industrial inspection, and medical image analysis. Although larger in parameters and computation, YOLOv13-L maintains near real-time speed with the support of GPUs, NPUs, and optimized inference frameworks.

Source model

• Input shape: 1x3x640x640
• Number of parameters: 61.04M
• Model size: 244.50M
• Output shape: 1x84x8400

Source model repository: yolov13

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