mlgidDETECT

This package is included in the mlgidBASE package and can be used as part of the mlgid pipeline.

Clone repository

• Clone with ssh (recommended) git clone git@github.com:mlgid-project/mlgidDETECT.git
• If it fails, use https: git clone https://github.com/mlgid-project/mlgidDETECT.git

Installation

Install Conda environment (recommended)

• Install miniconda https://docs.anaconda.com/miniconda/#quick-command-line-install

• Move into directory: cd mlgidDETECT

• (Option 1) Create environment with CPU and optional GPU inference
  cd setup
conda env create -f conda_cpu.yaml
conda activate mlgiddetect-cpu\

• (Option 2) Create environment with with additional GPU preprocessing
  cd setup
python setup_cuda.py
conda activate mlgiddetect-gpu
conda env config vars set LD_LIBRARY_PATH=${CONDA_PREFIX}/lib:${LD_LIBRARY_PATH}
conda deactivate
conda activate mlgiddetect-gpu
  Set PREPROCESSING CUDA: True in the config file

Install package with pip

• Install package
  pip install mlgiddetect

Usage

With a PyGIDDataset

python main.py --input_dataset=/home/testuser/dataset.h5

With a single image

python main.py --image_path=./w4_mapbbr32.tif

With a config file

python main.py --config_file=./faster_rcnn.yaml

Using the PyPI package

Use mlgidDETECT_tutorial.ipynb to get started.

GPU support

The pip package depends on the CPU build of ONNX Runtime, which works on every machine. For GPU inference, replace it with the GPU build (never install both at the same time, they share the same onnxruntime module and overwrite each other):

pip uninstall -y onnxruntime
pip install onnxruntime-gpu

Note that current onnxruntime-gpu wheels require the CUDA 13 runtime (libcudart.so.13); on machines with a CUDA 12 driver, install an onnxruntime-gpu version built for CUDA 12 instead. If the GPU build is installed and CUDA is available, it is automatically used for inference. To use CUDA for preprocessing, use the install instructions for GPU support.