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Create project_info.py
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apps/gradio_app/project_info.py
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CONTENT_DESCRIPTION = """
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**SlimFace: Advanced Face Classification with TorchVision Backbones**
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"""
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CONTENT_IN_1 = """
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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.
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"""
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CONTENT_IN_2 = """
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<p class="source">
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For more information, you can follow below:<br>
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Source code:
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<a class="badge" href="https://github.com/danhtran2mind/SlimFace">
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<img src="https://img.shields.io/badge/GitHub-danhtran2mind%2FSlimFace-blue?style=flat" alt="GitHub Repo">
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</a>,
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Author:
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<a class="badge" href="https://github.com/danhtran2mind">
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<img src="https://img.shields.io/badge/GitHub-danhtran2mind-blue?style=flat" alt="GitHub Profile">
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</a>,
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PyTorch Docs:
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<a class="badge" href="https://docs.pytorch.org/vision/main/models.html">
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<img src="https://img.shields.io/badge/PyTorch-Pretrain%20Model%20Docs-blue?style=flat" alt="PyTorch Docs">
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</a>
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</p>
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"""
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CONTENT_OUTTRO = """
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## More Information about SlimFace
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"""
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CONTENT_OUT_1 = """
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<div class="quote-container">
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<p>
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This project leverages code from
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<a class="badge" href="https://github.com/otroshi/edgeface">
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<img src="https://img.shields.io/badge/Built%20on-otroshi%2Fedgeface-blue?style=flat&logo=github" alt="Built on edgeface">
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</a>
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by
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<a class="badge" href="https://github.com/otroshi">
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<img src="https://img.shields.io/badge/GitHub-Hatef_Otroshi-blue?style=flat&logo=github" alt="Hatef Otroshi">
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</a>,
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with our own bug fixes and enhancements available at
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<a class="badge" href="https://github.com/danhtran2mind/edgeface/tree/main/face_alignment">
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<img src="https://img.shields.io/badge/GitHub-danhtran2mind%2Fedgeface-blue?style=flat&logo=github" alt="Edgeface Enhancements">
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</a>.
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</p>
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</div>
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"""
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CONTENT_OUT_2 = """
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**Supported Architectures:**
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- **EfficientNet**: B0-B7 and V2 (Small, Medium, Large) for balanced performance and efficiency. 📸
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- **RegNet**: X/Y series (400MF to 128GF) for optimized computation across diverse hardware. 💻
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- **Vision Transformers (ViT)**: B_16, B_32, H_14, L_16, L_32 for cutting-edge feature extraction. 🚀
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"""
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