EdgeYOLO has revolutionized the world of object detection on edge devices, allowing for efficient and effective performance. In this article, we’ll guide you through the steps to set up and utilize EdgeYOLO effectively.

Introduction

In embedded devices like the Nvidia Jetson AGX Xavier, EdgeYOLO achieves an impressive 34 FPS with an average precision (AP) of 50.6% on the COCO2017 dataset. This makes it a superb choice for real-time applications. Particularly, the smaller EdgeYOLO-S model reaches 53 FPS and an impressive AP of 63.3% for small models in COCO2017.

Setup

Let’s dive into the setup process!

• Clone the repository:

  git clone https://github.com/LSH9832/edgeyolo.git

• Navigate into the directory:

  cd edgeyolo

• Install required libraries:

  pip install -r requirements.txt

• For TensorRT users: Ensure you have torch2trt and TensorRT Development Toolkit (version 7.1.3.0) installed.

Inference

Once you have set up the environment, you can test the model by running inference.

• Download the weights: Download here
• Run the detection:

  python detect.py --weights edgeyolo_coco.pth --source video.mp4 --fp16

Training and Evaluation

To train your model, prepare your dataset and create a configuration file:

• Prepare your dataset: Follow the correct format (COCO, YOLO, etc.) and create your dataset.yaml file.
• Execute Train:

  python train.py --cfg .params/train/train_XXX.yaml

• Evaluate your model:

  python evaluate.py --weights edgeyolo_coco.pth --dataset params/dataset_XXX.yaml --batch 16 --device 0

Analogy: Understanding EdgeYOLO’s Functionality

Imagine EdgeYOLO as a highly skilled courier delivering packages through a busy city (your dataset). Just as a courier must navigate around obstacles (like traffic or roadblocks), EdgeYOLO processes images to accurately detect and classify objects while maximizing speed (FPS) and accuracy (AP). The deployment on edge devices like Nvidia Jetson is like giving that courier a scooter, significantly improving their ability to get the packages delivered faster, even in a busy environment.

Troubleshooting

If you encounter issues during installation or execution, here are some tips:

• If you receive a CUDA error during training, consider reducing your PyTorch version to 1.8.0.
• For DOTA datasets, ensure you’re using a single GPU training mode.
• If your TensorRT model loses precision, revert to TensorRT version 7.X.X.X or 8.2.X.X.

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Conclusion

EdgeYOLO paves the way for real-time object detection on edge devices. By following these setup and utilization instructions, you can harness its full potential for your own applications.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.