This repository is a structured exploration of classical and modern computer vision architectures implemented in PyTorch.
The goal of this project is to deeply understand the evolution of convolutional and attention-based models — from AlexNet to Vision Transformers — by implementing them modularly and analyzing their architectural trade-offs.
- AlexNet
- VGG
- ResNet
- DenseNet
- GoogLeNet (Inception v1)
- MobileNet
- SqueezeNet
- Vision Transformer (ViT)
- Convolution operations (from scratch implementation)
- Regularization techniques
- Transfer learning strategies
- DBNet (text detector) — implementation and pretrained weights are available under
DBNet/. This repository includes adbnet_icdar_best.pthcheckpoint that theOCRpipeline uses for text localization. - CRNN (text recognizer) — recognition model and training/inference notebooks are in
CRNN/(seeCRNN/README.md). - OCR pipeline — end-to-end detection + recognition notebooks are in
OCR/and demonstrate DBNet -> crop -> CRNN flow.
This repository was created to:
- Understand architectural innovations in deep learning
- Analyze parameter efficiency vs performance trade-offs
- Compare convolution-based and attention-based approaches
- Explore generalization techniques in deep neural networks
- Residual connections mitigate vanishing gradients
- Dense connectivity encourages feature reuse
- Depthwise separable convolutions reduce computational cost
- Transformers remove spatial locality bias but require larger datasets
- Regularization techniques significantly improve generalization
- Python
- PyTorch
- NumPy
- Hybrid CNN-Transformer architectures
- 3D CNNs for hyperspectral imagery
- Vision-Language Models (VLMs)
- Self-Supervised Pretraining methods
This repository was created as a structured study of deep learning architectures to understand their mathematical foundations and architectural evolution.