This tutorial follows the guide available at the following link:
https://github.com/Xilinx/Vitis-AI-Copyleft-Model-Zoo.git

Prerequisites

• Install Ubuntu 20.04.
• Install NVIDIA CUDA (if you want to use a GPU).
• Install Docker from the link below:
  https://docs.docker.com/desktop/install/ubuntu/

Steps

1. Clone the latest Vitis AI repository:

   git clone https://github.com/Xilinx/Vitis-AI
   cd Vitis-AI

2. Clone the Copyleft Model Zoo repository and copy the YOLOv7 folder to the Vitis-AI directory:

   git clone https://github.com/Xilinx/Vitis-AI-Copyleft-Model-Zoo.git
   cp -r Vitis-AI-Copyleft-Model-Zoo/yolov7 Vitis-AI/

3. Download the latest Vitis AI Docker container (CPU version):

   docker pull xilinx/vitis-ai-cpu:latest

4. Run the Docker container:

   ./docker_run.sh xilinx/vitis-ai-cpu:latest

5. Activate the Vitis AI environment:

   conda activate vitis-ai-pytorch

6. Install YOLOv7 requirements:

   !pip install -r yolov7/requirements.txt

7. Update g++:

   !sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
   !sudo apt install -y g++-11

8. Get the COCO dataset:

   %cd yolov7/
   !bash scripts/get_coco.sh
   %cd ../

   To use a custom dataset, annotate the dataset using LabelImg or other software. Then follow the tutorial linked below to prepare it for training:
   https://www.youtube.com/watch?v=GRtgLlwxpc4

9. Download the YOLOv7 training model from GitHub and place it in the yolov7 folder:

   cd yolov7 
   wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt

10. Train with quantization-aware training (QAT):
    Keep --device 0 if running on GPU; otherwise, remove it.

%cd yolov7/
!python train_qat.py --workers 16 --device 0 --epochs 100 --batch-size 8 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights yolov7_training.pt --name yolov7-qat --hyp data/hyp.scratch.p5_qat.yaml --nndct_convert_sigmoid_to_hsigmoid --nndct_convert_silu_to_hswish 
%cd ../

11. Dump the model:

%cd yolov7/
!python test_nndct.py --data data/coco.yaml --img 640 --batch 1 --conf 0.001 --iou 0.65 --device 0 --weights ../quantized/qat_09.pt --name yolov7_640_val --quant_mode test --nndct_qat --nndct_convert_sigmoid_to_hsigmoid --nndct_convert_silu_to_hswish --dump_model
%cd ../