README.md

YOLOX: Exceeding YOLO Series in 2021

Preparation steps before running a TensorRT demo

1. set up a virtual environment.

   git clone https://github.com/Megvii-BaseDetection/YOLOX.git
   cd YOLOX
   conda create -n yolox -y python=3.11
   conda activate yolox
   pip install torch torchvision --index-url https://download.pytorch.org/whl/cu129
   
   # comment line 18 in requirements.txt (# onnx-simplifier==0.4.10)
   pip install -v -e .
   pip install opencv-python
   pip install onnx
   pip install onnxscript
   pip install onnxsim
   

2. download pretrained checkpoints.

   mkdir -p pretrained
   wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth -P pretrained
   

3. run demo from original repository

   python tools/demo.py image -n yolox-s -c checkpoints/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device gpu
   

DEMOS

1. YOLOX Python Version

2. YOLOX Cpp Version

3. YOLOX Cpp Version 2

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• YOLOX: Exceeding YOLO Series in 2021
• YOLOX official GitHub