🔍 Vision Model Evaluation Framework (PyTorch)

A scalable framework for benchmarking deep convolutional architectures on classification tasks. This project evaluates the impact of optimizers, learning rates, and batch sizes across multiple CNN backbones — providing a reproducible experimental setup aligned with research and production best practices.

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🚀 Key Highlights

• Plug-and-play support for multiple CNN architectures
• Scalable benchmarking with grid search over:
  • Optimizers: SGD, Adam
  • Learning Rates: 0.01, 0.1
  • Batch Sizes: 32, 64
• Data augmentation, normalization, and stratified validation split
• Detailed metrics logging + real-time visualization support
• Model weights saved automatically for top-performing configs

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🧠 Architectures Supported

• Custom Lightweight CNN (baseline)
• ResNet-18
• MobileNetV2
• GoogleNet
• AlexNet (included for completeness; not recommended for production)

🛡️ Modular design allows easy plug-in of ViT, EfficientNet, ConvNext, etc.

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📊 Experiments & Logging

• Validation and test performance tracked across all combinations
• Key metrics:
  • Train & Val Accuracy/Loss (per epoch)
  • Best Validation Accuracy (per config)
  • Final Test Accuracy (per model)
• Automatic selection of best model per architecture
• Visual analytics:
  • Accuracy trends
  • Impact of learning rate
  • Batch size comparison
  • Model vs Optimizer performance

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🔁 Use Cases

• Model architecture benchmarking
• Optimizer sensitivity studies
• Lightweight deployment model search
• Academic reproducibility experiments