/acne_detection

acne_detection

Primary LanguagePythonMIT LicenseMIT

acne_detection

acne_detection

ran in python 3.6.8

Requirements

  • tensorflow == 1.13.1

Training

export PYTHONPATH=$PYTHONPATH:/PATH_TO_THE_PROJECT/slim/

nohup python3.6 object_detection/model_main.py --pipeline_config_path=faster_rcnn_resnet101_coco.config --model_dir=./saved_models/ --num_train_steps=20000 --num_eval_steps=2000 --alsologtostderr > acne_train.log &

Exporting the model

python3.6 object_detection/export_inference_graph.py --input_type image_tensor --pipeline_config_path faster_rcnn_resnet101_coco.config --trained_checkpoint_prefix ./saved_models/model.ckpt-xxxxx --output_directory ./latest_models/

Pretrained model

A pretrained model (faster_rcnn_resnet101) can be found at MEGA, another (faster_rcnn_inception_v2) at MEGA.

P.S. MEGA is the best cloud drive I've ever used. Strong recommendation for it.

Citation

@article{thc_2022_acne_detection,
      title = {{Acne Detection and Severity Evaluation with Interpretable Convolutional Neural Network Models}},
     author = {Wen, Hao and Yu, Wenjian and Wu, Yuanqing and Zhao, Jun and Liu, Xiaolong and Kuang, Zhexiang and Fan, Rong},
    journal = {Technology and Health Care},
        doi = {10.3233/thc-228014},
       issn = {1878-7401},
       year = {2022},
      month = {2},
  publisher = {{IOS Press}},
     volume = {30},
      pages = {143--153}
}