YOLOv7 is the latest version of the YOLO (You Only Look Once) series, released in 2022. It significantly improves both accuracy and speed for object detection tasks, making it one of the most efficient real-time object detection models. YOLOv7 introduces new architectural designs and optimization techniques, such as Extended Efficient Layer Aggregation Networks (E-ELAN) and Task-Aware Planning (TTA), enhancing detection accuracy while maintaining fast inference speed. It performs efficient object detection with minimal computational resources, making it suitable for applications like autonomous driving, video surveillance, and smart manufacturing. YOLOv7 marks a major advancement in the field of real-time object detection.

Source model

• Input shape: 640x640
• Number of parameters: 35.19M
• Model size: 144.73M
• Output shape: 1x25200x85

Source model repository: YOLOv7

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