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Dyuti Dasmahapatra
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Updated README and app emoji fix
Browse files- README.md +8 -8
- app.py +1 -1
- runtime.txt +1 -0
README.md
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**A Comprehensive Explainability and Validation Dashboard for Vision Transformers**
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[π Live Demo](
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<img src="assets/dashboard.png" alt="Dashboard Preview" width="800"/>
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The **ViT Auditing Toolkit** is an advanced, interactive dashboard designed to help researchers, ML practitioners, and AI auditors understand, validate, and improve Vision Transformer (ViT) models. It provides a comprehensive suite of explainability techniques and auditing tools through an intuitive web interface.
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###
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This toolkit is designed as an **Explainable AI (XAI) and Human-Centered AI (HCAI) analysis tool** to help you:
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We encourage researchers and practitioners to use this toolkit as a **starting point** for deeper investigation into model behavior, complementing it with rigorous testing protocols and domain expertise.
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###
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- **π Transparency**: Understand what your ViT models actually "see" and learn
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- **β
Validation**: Verify model reliability through systematic testing
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Try the toolkit instantly on Hugging Face Spaces:
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### π [Launch Interactive Demo](https://huggingface.co/spaces/
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*No installation required! Upload an image and start exploring.*
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##
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The project includes **20 curated test images** organized by analysis type:
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##
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<div align="center">
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##
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- **[QUICKSTART.md](QUICKSTART.md)** - Get started in 5 minutes
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- **[TESTING.md](TESTING.md)** - Comprehensive testing guide with 22 test cases
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##
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This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.
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**A Comprehensive Explainability and Validation Dashboard for Vision Transformers**
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[π Live Demo](https://huggingface.co/spaces/Dyra1204/ViT-Auditing-Toolkit) | [π Documentation](#features) | [π‘ Examples](#usage-guide) | [π€ Contributing](#contributing)
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<img src="assets/dashboard.png" alt="Dashboard Preview" width="800"/>
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The **ViT Auditing Toolkit** is an advanced, interactive dashboard designed to help researchers, ML practitioners, and AI auditors understand, validate, and improve Vision Transformer (ViT) models. It provides a comprehensive suite of explainability techniques and auditing tools through an intuitive web interface.
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### π― Purpose & Scope
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This toolkit is designed as an **Explainable AI (XAI) and Human-Centered AI (HCAI) analysis tool** to help you:
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We encourage researchers and practitioners to use this toolkit as a **starting point** for deeper investigation into model behavior, complementing it with rigorous testing protocols and domain expertise.
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### π Why This Toolkit?
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- **π Transparency**: Understand what your ViT models actually "see" and learn
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- **β
Validation**: Verify model reliability through systematic testing
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Try the toolkit instantly on Hugging Face Spaces:
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### π [Launch Interactive Demo](https://huggingface.co/spaces/Dyra1204/ViT-Auditing-Toolkit)
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*No installation required! Upload an image and start exploring.*
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---
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## πΌοΈ Test Images Included
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The project includes **20 curated test images** organized by analysis type:
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## πΈ Screenshots
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<div align="center">
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## π Additional Resources
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- **[QUICKSTART.md](QUICKSTART.md)** - Get started in 5 minutes
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- **[TESTING.md](TESTING.md)** - Comprehensive testing guide with 22 test cases
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## π License
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This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.
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app.py
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border: 1px solid rgba(99, 102, 241, 0.15);
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">
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<h2 style="font-size: 1.75rem; font-weight: 700; color: #e0e7ff; margin-bottom: 1rem;">
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</h2>
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<p style="color: #94a3b8; line-height: 1.8; font-size: 1.05rem; margin-bottom: 1.5rem;">
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border: 1px solid rgba(99, 102, 241, 0.15);
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">
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<h2 style="font-size: 1.75rem; font-weight: 700; color: #e0e7ff; margin-bottom: 1rem;">
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βΉοΈ About This Toolkit
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</h2>
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<p style="color: #94a3b8; line-height: 1.8; font-size: 1.05rem; margin-bottom: 1.5rem;">
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runtime.txt
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python-3.10
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