README.md

DEIM: DETR with Improved Matching for Fast Convergence

How to Run

1. set up a virtual environment.

   git clone https://github.com/Intellindust-AI-Lab/DEIM.git
   cd DEIM
   conda create -n deim -y python=3.11
   conda activate deim
   pip install torch torchvision --index-url https://download.pytorch.org/whl/cu129
   pip install opencv-python
   pip install onnx
   pip install onnxscript
   pip install onnxsim
   pip install gdown
   pip install -r requirements.txt
   

2. download pretrained checkpoints.

   mkdir -p pretrained
   gdown --fuzzy https://drive.google.com/file/d/153_JKff6EpFgiLKaqkJsoDcLal_0ux_F/view?usp=drive_link -O pretrained/
   

3. check pytorch model inference

   cd ..
   python infer.py
   

How to Run (TensorRT)

1. generate onnx file

   python onnx_export.py
   

2. generate tensorrt model

   python onnx2trt.py
   

• fp16 (DEIM-RT-DETRv2-S)
  [TRT_E] 1000 iterations time: 6.0511 [sec]
  [TRT_E] Average FPS: 165.26 [fps]
  [TRT_E] Average inference time: 6.05 [msec]
  GPU mem : 236M

• DEIM: DETR with Improved Matching for Fast Convergence

• DEIM official GitHub