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CONTENT_DESCRIPTION = """
**SlimFace: Advanced Face Classification with TorchVision Backbones**
"""
CONTENT_IN_1 = """
SlimFace empowers developers to build high-accuracy face classification models by leveraging TorchVision's powerful pre-trained architectures through a transfer learning approach. 🌟 It provides a flexible, efficient, and scalable solution for facial recognition, supporting training on custom datasets and inference to deliver top-tier performance for custom applications.
"""
CONTENT_IN_2 = """
<p class="source">
For more information, you can follow below:<br>
Source code:
<a class="badge" href="https://github.com/danhtran2mind/SlimFace">
<img src="https://img.shields.io/badge/GitHub-danhtran2mind%2FSlimFace-blue?style=flat?logo=github" alt="GitHub Repo">
</a>,
Author:
<a class="badge" href="https://github.com/danhtran2mind">
<img src="https://img.shields.io/badge/GitHub-danhtran2mind-blue?style=flat?logo=github" alt="GitHub Profile">
</a>,
PyTorch Docs:
<a class="badge" href="https://docs.pytorch.org/vision/main/models.html">
<img src="https://img.shields.io/badge/PyTorch-Pretrain%20Model%20Docs-orange?style=flat?logo=pytorch" alt="PyTorch Docs">
</a>
</p>
"""
CONTENT_OUTTRO = """
## More Information about SlimFace
"""
CONTENT_OUT_1 = """
<div class="quote-container">
<p>
This project leverages code from
<a class="badge" href="https://github.com/otroshi/edgeface">
<img src="https://img.shields.io/badge/Built%20on-otroshi%2Fedgeface-blue?style=flat&logo=github" alt="Built on edgeface">
</a>
by
<a class="badge" href="https://github.com/otroshi">
<img src="https://img.shields.io/badge/GitHub-Hatef_Otroshi-blue?style=flat&logo=github" alt="Hatef Otroshi">
</a>,
with our own bug fixes and enhancements available at
<a class="badge" href="https://github.com/danhtran2mind/edgeface/tree/main/face_alignment">
<img src="https://img.shields.io/badge/GitHub-danhtran2mind%2Fedgeface-blue?style=flat&logo=github" alt="Edgeface Enhancements">
</a>.
</p>
</div>
"""
CONTENT_OUT_2 = """
**Supported Architectures:**
- **EfficientNet**: B0-B7 and V2 (Small, Medium, Large) for balanced performance and efficiency. 📸
- **RegNet**: X/Y series (400MF to 128GF) for optimized computation across diverse hardware. 💻
- **Vision Transformers (ViT)**: B_16, B_32, H_14, L_16, L_32 for cutting-edge feature extraction. 🚀
""" |