Official codes for Efficient Segmentation with Texture in Ore Images Based on Box-supervised Approach. You can see details in [Paper].

Installation

First install Detectron2 following the official guide: INSTALL.md.

Please use Detectron2 with commit id 9eb4831 if you have any issues related to Detectron2.

Then build OREINST with:

git clone https://github.com/aim-uofa/AdelaiDet.git
cd OREINST
python setup.py build develop

If you are using docker, a pre-built image can be pulled with:

docker pull tianzhi0549/adet:latest

Train Your Own Models

To train a model with "train_net.py", first setup the corresponding datasets following datasets/README.md, then run:

sh train.sh

To evaluate the model after training, run:

sh eval.sh

Note that:

• The configs are made for 1-GPU training. To train on another number of GPUs, change the --num-gpus.
• If you want to measure the inference time, please change --num-gpus to 1.
• We set OMP_NUM_THREADS=0 by default, which achieves the best speed on our machines, please change it as needed.

Citing

If you find this repository useful in your research, please consider citing:

@ARTICLE{OREINST2023,  
  author={Guodong Sun and Delong Huang and Yuting Peng and Le Cheng and Bo Wu and Yang Zhang},  
  booktitle={Engineering Applications of Artificial Intelligence},   
  title={Efficient Segmentation with Texture in Ore Images Based on Box-supervised Approach},   
  year={2024},
  pages={1-14}
  }