Unsupervised Landmark Discovery via Consistency-Guided Bottleneck

Requirements

• Python 3.8 or later
• PyTorch 1.8 with torchvision
• OpenCV

Datasets

• CelebA can be obtained from here. MAFL (training & test) is included. Bounding box obtained to crop the images is computed from the landmarks provided in the CelebA dataset. place the all files in the same folder.

• AFLW can be found here.

• 300W-LP dataset used for training can be found here. LS3D used for testing the corresponding, can be downloaded from here.

• Catshead Dataset can be found here.

• Shoes Dataset can be downloaded from here. Numerical results for shoes are not possible since there are no ground truth annotations.

Testing Pretrained Models

To test our the pretrained models, download from the links below. Create and place them in the folder ``pretrained_models_to_test"". Run the testing script 'test_pretrained_model_script.sh'.

Pretrained Models

Pretrained models are provided here.

Training / Testing

To train/test our method use the corresponding command in the provided training/testing script.