CAC:Confidence-Aware Co-training for Weakly Supervised Crack Segmentation

​A Pytorch implementation of Weakly Supervised Crack Segmentaion projects.

1. Datasets：Crack500, CrackForest, DeepCrack.
   Notes：please download the corresponding dataset and prepare it by following the guidance
2. Installation： You can create a new Conda environment using the command:

conda env create -f environment.yml

3. Training：

• Before the training, please download the dataset and copy it into the folder "datasets".
  --datasets
  ----crack500
  ----CrackForest
  ----DeepCrack
  
• Check the hyperparameters of CAC training in ./options/base_options.py and ./options/train_options.py.
• Training CAC model by meta_train_with_crack500.py

python meta_train_with_crack500.py

4. Testing:

• Check the hyperparameters of CAC testing in ./options/base_options.py and ./options/test_options.py.

python test_meta_with_crack500.py

Notes: the testing dataset name can be replaced in python file test_meta_with_crack500.py.

5. Evaluation：

cd eval
python eval.py --metric_mode prf --model_name crack500_CAM_proportion --output crack500_CAM_proportion.prf --f1_threshold_mode ois