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🚀 NeuralForge GUI - Quick Start

Launch the GUI

Windows (Easy Way)

# Double-click this file:
LAUNCH_GUI.bat

# Or run in terminal:
python tests\gui_test.py

Linux/Mac

chmod +x LAUNCH_GUI.sh
./LAUNCH_GUI.sh

How to Use (3 Steps)

1️⃣ Load Your Model

  1. Click "Use Default" button (loads models/final_model.pt)
  2. Make sure dataset is set to cifar10 (or your trained dataset)
  3. Click "Load Model"
  4. Wait for ✓ green checkmark

2️⃣ Select an Image

  1. Click "Browse" under Image Selection
  2. Choose any image file (JPG, PNG, etc.)
  3. Image preview appears automatically

3️⃣ Get Prediction

  1. Click "🔍 Predict" button
  2. See results instantly:
    • Main prediction in large green text
    • Confidence percentage
    • Top-5 predictions with visual bars

📸 Test with Your Own Images!

CIFAR-10 Classes:

  • ✈️ airplane
  • 🚗 automobile
  • 🐦 bird
  • 🐱 cat
  • 🦌 deer
  • 🐕 dog
  • 🐸 frog
  • 🐴 horse
  • 🚢 ship
  • 🚛 truck

Your Results:

  • Training Accuracy: 99.98%
  • Validation Accuracy: 75.81%
  • Model: ResNet18 (11.2M parameters)

🎨 GUI Features

Beautiful Dark Theme

  • Professional dark background
  • Green accent colors
  • Smooth animations
  • Easy-to-read fonts

Real-Time Feedback

  • Loading indicators
  • Progress bars
  • Status messages
  • Error handling

Smart Interface

  • Image preview
  • Model information display
  • Top-5 predictions with bars
  • Confidence percentages

💡 Pro Tips

  1. Load Once, Test Many: Load model once, test unlimited images
  2. Quick Testing: Use default button for instant model loading
  3. Best Results: Use clear, centered images
  4. Fast Predictions: First prediction initializes, then super fast!
  5. Check Info: Model info shows parameters and accuracy

🐛 Troubleshooting

"No module named 'PyQt6'"

pip install PyQt6

"Model file not found"

  • Train a model first: python train.py --dataset cifar10 --epochs 50
  • Or check models/ folder exists

GUI won't start

pip install --upgrade PyQt6

Prediction errors

  • Ensure dataset name matches training dataset
  • Check image file is valid
  • Verify model loaded successfully (green checkmark)

📊 What You Can Test

Example Images to Try:

For CIFAR-10:

  • Photos of cats, dogs, horses
  • Pictures of cars, trucks, airplanes
  • Images of ships, frogs, birds
  • Nature scenes with deer

Tips:

  • Use clear, single-object images
  • Centered subjects work best
  • Good lighting improves accuracy
  • Any image size works (auto-resized)

🎯 Expected Results

Based on your training:

  • High confidence (>90%): Clear images of trained classes
  • Medium confidence (50-90%): Partial views or similar classes
  • Low confidence (<50%): Unclear or out-of-distribution images

🚀 Next Steps

  1. Test Different Images: Try various images from each class
  2. Check Accuracy: Compare predictions with actual labels
  3. Train More: Improve model with more epochs for better accuracy
  4. Try Other Datasets: Load models trained on different datasets

📝 Example Session

1. Start GUI
2. Click "Use Default"
3. Click "Load Model"
   ✓ Model loaded successfully
4. Click "Browse" → Select cat.jpg
5. Click "🔍 Predict"
   
Results:
   🎯 cat
   Confidence: 94.3%
   
   Top-5:
   1. cat     ████████████████ 94.3%
   2. dog     ██ 3.2%
   3. deer    █ 1.5%
   4. bird    █ 0.7%
   5. frog    ░ 0.3%

🎉 Enjoy Testing Your AI!

Your model achieved 75.81% validation accuracy - test it on real images and see how it performs!

Questions or Issues?

  • Check tests/README_GUI.md for detailed documentation
  • Verify model file exists in models/ folder
  • Ensure PyQt6 is installed: pip list | grep PyQt6

Made with 🔥 by NeuralForge