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```yaml
---
language: en
license: mit
library_name: pytorch
tags:
- face-recognition
- self-supervised-learning
- contrastive-learning
datasets:
- YFCC-CelebA
- CelebA
---
```
# VCL: Variational Contrastive Learning for Face Understanding
VCL is a robust self-supervised learning method designed specifically for face understanding tasks, combining variational contrastive learning with beta-divergence to effectively handle noisy and unlabeled datasets[1].
## Model Details
### Model Description
**Developed by:** Mehmet Can Yavuz and Berrin Yanikoglu
**Model type:** Self-Supervised Variational Contrastive Learning with Applications to Face Understanding
**Language(s):** Python
**License:** MIT
**Model:** ResNet10t
## Uses
### Direct Use
The model is designed for:
- Face attribute recognition
- Face verification tasks
- Multi-label classification problems
- Learning from noisy and unlabeled datasets
## Model Architecture
The architecture consists of three main components:
- Feature extraction backbone (ResNet10t or VGG11bn)
- Gaussian sampling head for distribution learning
- Contrastive learning framework with augmentations
## Training Details
### Training Data
The model was pretrained on the YFCC-CelebA dataset and you can fine-tune on CelebA dataset.
### Training Procedure
#### Training Hyperparameters
**Training regime:**
- Optimizer: AdamW
- Learning rate: 1e-3
- Weight decay: 0.01
- Batch size: 128
- Temperature: 0.07
- Beta: 0.005
## Evaluation
### Results
Performance on CelebA test set with different pretraining approaches:
| Setting | ResNet10t (1%) | VGG11bn (1%) | ResNet10t (10%) | VGG11bn (10%) |
|---------|----------------|---------------|-----------------|---------------|
| VCL | 0.5836 | 0.5719 | 0.6848 | 0.6796 |
| VCL (beta) | 0.5998 | 0.5958 | 0.7098 | 0.6998 |
## How to Get Started with the Model
```python
# Installation
git clone https://github.com/convergingmachine/VCL
cd VCL
pip install -r requirements.txt
# Training
python train_beta.py
## Citation
```bibtex
@INPROCEEDINGS{10582001,
author={Yavuz, Mehmet Can and Yanikoglu, Berrin},
booktitle={2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)},
title={Self-Supervised Variational Contrastive Learning with Applications to Face Understanding},
year={2024},
pages={1-9},
doi={10.1109/FG59268.2024.10582001}}
```
## Model Card Contact
For questions about this model, please open an issue in the GitHub repository. |