7Gen - Advanced MNIST Digit Generation System

State-of-the-art Conditional GAN for MNIST digit synthesis with self-attention mechanisms.

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🚀 Features

• 🎯 Conditional Generation: Generate specific digits (0–9) on demand.
• 🖼️ High Quality Output: Sharp and realistic handwritten digit samples.
• ⚡ Fast Inference: Real-time generation on GPU.
• 🔌 Easy Integration: Minimal setup, PyTorch-native implementation.
• 🚀 GPU Acceleration: Full CUDA support.

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🔍 Model Details

• Architecture: Conditional GAN with self-attention
• Parameters: 2.5M
• Input: 100-dimensional noise vector + class label
• Output: 28x28 grayscale images
• Training Data: MNIST dataset (60,000 images)
• Training Time: ~2 hours on NVIDIA RTX 3050 Ti

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🧪 Performance Metrics

Metric	Score
FID Score	12.3
Inception Score	8.7

• Training Epochs: 100
• Batch Size: 64

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⚙️ Training Configuration

model:
  latent_dim: 100
  num_classes: 10
  generator_layers: [256, 512, 1024]
  discriminator_layers: [512, 256]

training:
  batch_size: 64
  learning_rate: 0.0002
  epochs: 100
  optimizer: Adam
  beta1: 0.5
  beta2: 0.999