INDEX.md

🎯 CIFAR-100 Classifier - Complete Package Index

🌟 Welcome!

Your CIFAR-100 Image Classifier is ready for deployment to Hugging Face Spaces!

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📖 Documentation - Read in This Order

#	File	Purpose	When to Read
1️⃣	START_HERE.md	🚀 First-time setup & orientation	Start here!
2️⃣	QUICKSTART.md	⚡ Quick reference guide	Need fast overview
3️⃣	DEPLOYMENT_GUIDE.md	📋 Step-by-step deployment	Ready to deploy
4️⃣	FILES_EXPLAINED.md	📚 What each file does	Want to understand structure
5️⃣	PROJECT_SUMMARY.md	📊 Complete project overview	Need full details
6️⃣	COMPLETE_SETUP_SUMMARY.txt	✅ Setup checklist	Final verification

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🎯 Choose Your Path

🏠 I want to test locally first

→ Read: START_HERE.md
→ Run: python test_app_locally.py
→ Or double-click: run_local.bat (Windows)

☁️ I want to deploy immediately

→ Read: DEPLOYMENT_GUIDE.md
→ Quick: QUICKSTART.md (3-step deployment)

🤔 I want to understand the code

→ Read: FILES_EXPLAINED.md
→ Review: app.py and model.py

🎨 I want to customize the app

→ Read: PROJECT_SUMMARY.md (Customization section)
→ Edit: app.py (UI/styling)
→ Edit: README.md (Space description)

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📦 What You Get

┌─────────────────────────────────────────────────────┐
│  CIFAR-100 Image Classifier                         │
│  Web Application with Streamlit                     │
├─────────────────────────────────────────────────────┤
│  ✨ Features:                                        │
│  • Upload images → Get predictions                  │
│  • 100 class classification                         │
│  • Confidence scores & probabilities                │
│  • Interactive charts                               │
│  • Top-K predictions                                │
│  • Download results                                 │
│                                                     │
│  🤖 Model:                                           │
│  • ResNet-34 architecture                           │
│  • ~21M parameters                                  │
│  • Trained on CIFAR-100                             │
│  • Production-ready                                 │
│                                                     │
│  🚀 Deployment:                                      │
│  • Hugging Face Spaces ready                        │
│  • Streamlit-powered                                │
│  • Git LFS configured                               │
│  • Complete documentation                           │
└─────────────────────────────────────────────────────┘

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🗂️ File Organization

🎯 Core Files (6) - Required for HF Spaces

app.py                  ← Main application
model.py                ← Neural network architecture
cifar100_model.pth      ← Trained weights (93 MB)
requirements.txt        ← Dependencies
README.md               ← HF Space homepage
.gitattributes         ← Git LFS config

🛠️ Development Files (4) - For local testing

test_app_locally.py     ← Automated testing
run_local.bat           ← Windows launcher
run_local.sh            ← Linux/Mac launcher
.gitignore             ← Git ignore rules

📚 Documentation Files (7) - Helpful guides

START_HERE.md           ← Begin here
QUICKSTART.md           ← Fast reference
DEPLOYMENT_GUIDE.md     ← Detailed steps
FILES_EXPLAINED.md      ← File descriptions
PROJECT_SUMMARY.md      ← Complete overview
COMPLETE_SETUP_SUMMARY.txt  ← Checklist
INDEX.md                ← This file

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⚡ Ultra-Quick Start

5 minutes to deployment:

# Step 1: Test locally (30 seconds)
python test_app_locally.py

# Step 2: Create HF Space (1 minute)
# Go to: https://huggingface.co/new-space
# Choose: Streamlit SDK

# Step 3: Upload files (2 minutes)
# Drag & drop all 6 core files via web UI
# OR use git clone and push

# Step 4: Wait for build (2 minutes)
# Check build logs, wait for "Running"

# Step 5: Test your deployed app! ✅
# Visit your space URL

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🎨 App Preview

When users visit your app, they'll see:

┌──────────────────────────────────────────────────────┐
│ 🖼️ CIFAR-100 Image Classifier                        │
├──────────────────────────────────────────────────────┤
│                                                      │
│  📤 Upload Image     │  🎯 Classification Results    │
│                      │                               │
│  [Upload Button]     │  Predicted Class              │
│                      │  ┌─────────────────────────┐  │
│  [Your Image Here]   │  │   DOLPHIN               │  │
│                      │  │   Confidence: 87.45%    │  │
│  📐 Size: 800×600    │  └─────────────────────────┘  │
│  🎨 Mode: RGB        │                               │
│                      │  📊 Top 5 Predictions:        │
│                      │  1. dolphin      87.45%       │
│                      │  2. whale        5.23%        │
│                      │  3. seal         3.12%        │
│                      │  4. shark        1.87%        │
│                      │  5. aquarium_fish 0.95%       │
│                      │                               │
│                      │  [Interactive Chart]          │
│                      │  [Download Results]           │
└──────────────────────────────────────────────────────┘

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🎯 Success Criteria

Your deployment is successful when:

✅ App loads at your Hugging Face URL ✅ Can upload images without errors ✅ Predictions are generated correctly ✅ Confidence scores display (0-100%) ✅ Top predictions shown ✅ Charts render properly ✅ Download button works

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📊 File Statistics

Total Files:     14 files
Total Size:      ~94 MB
Core Files:      6 (required)
Test Files:      4 (optional)
Docs:            7 (helpful)

Largest File:    cifar100_model.pth (93 MB)
Code Files:      app.py (16 KB) + model.py (7 KB)
Dependencies:    6 packages (requirements.txt)

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💡 Pro Tips

1. Always test locally first - Saves time debugging on HF
2. Use Git LFS - Essential for model files >10MB
3. Check logs - HF provides detailed build/runtime logs
4. Start with CPU - Free tier works great for demos
5. Monitor usage - HF shows analytics for your space

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🎓 Learning Resources

• Streamlit Tutorial: Learn to customize the UI
• HF Spaces Docs: Understand deployment options
• Git LFS Guide: Master large file handling

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🚀 Ready to Go?

For Testing:

python test_app_locally.py

For Quick Start:

→ Open: START_HERE.md

For Deployment:

→ Open: DEPLOYMENT_GUIDE.md

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📞 Need Help?

1. Check relevant .md file for your question
2. Read COMPLETE_SETUP_SUMMARY.txt for checklist
3. Review Hugging Face Spaces documentation
4. Check Streamlit documentation

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🎉 Everything is ready! Time to deploy your AI app! 🚀

Created: October 10, 2025
Author: Krishnakanth
Project: CIFAR-100 Image Classifier for Hugging Face Spaces