d-li14 / face-attribute-prediction

Face Attribute Prediction on CelebA benchmark with PyTorch Implementation

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face-attribute-prediction

Face Attribute Prediction on CelebA benchmark with PyTorch Implemantation, heavily borrowed from my MobileNetV2 implementation.

Dependencies

  • Anaconda3 (Python 3.6+, with Numpy etc.)
  • PyTorch 0.4+
  • tensorboard, tensorboardX

Dataset

CelebA dataset is a large-scale face dataset with attribute-based annotations. Cropped and aligned face regions are utilized as the training source. For the pre-processed data and specific split, please feel free to contact me: d-li14@mails.tsighua.edu.cn

Features

  • Both ResNet and MobileNet as the backbone for scalability
  • Each of the 40 annotated attributes predicted with multi-head networks
  • Achieve ~92% average accuracy, comparative to state-of-the-art
  • Fast convergence (5~10 epochs) through finetuning the ImageNet pre-trained models

About

Face Attribute Prediction on CelebA benchmark with PyTorch Implementation

License:MIT License


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Language:Python 100.0%