If you want to harness the power of the YOLOv7 model for object detection with ONNX, you’ve landed in the right place! This guide takes you through every detail of the installation and execution processes, ensuring that even beginners can join the ranks of object detection experts.

Requirements

Before starting, make sure you have the necessary requirements. You can check the requirements.txt file for specifics.

• If you’re using a NVIDIA GPU, you’ll need to install onnxruntime-gpu.
• For other setups, just use onnxruntime.

Installation Steps

Ready to get started? Follow these steps:

1. Clone the repository:

git clone https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection.git

2. Navigate into the cloned directory:

cd ONNX-YOLOv7-Object-Detection

3. Install required packages:

pip install -r requirements.txt

Setting Up ONNX Runtime

Depending on your hardware, use the corresponding command to install ONNX Runtime:

• NVIDIA GPU computers:

pip install onnxruntime-gpu

• Otherwise:

pip install onnxruntime

Preparing the ONNX Model

The original YOLOv7 models are converted to various formats (including .onnx) by PINTO0309. You can download the models from his repository. Choose one of the following methods to download:

• Run download_single_batch.sh script.
• Copy the Google Drive link provided in the script and paste it into your browser to manually download the files.

Once downloaded, extract the model files (e.g., yolov7-tiny_480x640.onnx) to your models directory.

Running Object Detection

Now that everything is in place, you can run the object detection scripts:

• For image inference:

  python image_object_detection.py

• For webcam inference:

  python webcam_object_detection.py

• For video inference:

  python video_object_detection.py

Understanding the Code with an Analogy

Imagine you’re setting up a shop. The first thing you would do is make sure you have all the supplies and create a display area. In this analogy, the installation of packages corresponds to gathering supplies, while setting up the ONNX model is akin to arranging those supplies on shelves for easy access.

Once your shop is ready, you begin serving customers. Running the object detection script can be seen as opening up the shop for business. Just like helping customers find what they need in your shop, the script identifies objects in images or videos, providing you with outputs that tell you what’s where!

Troubleshooting

Facing issues? Here are some common troubleshooting steps:

• Ensure all dependencies are correctly installed as per requirements.txt.
• Check if the ONNX model is correctly placed in the models directory and that filenames in your scripts match those of the downloaded models.
• If you’re using a GPU but it’s not being detected, verify that your CUDA and cuDNN installations are properly set up.

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