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This project presents a fingerprint matching system utilizing deep learning. It features multiple models, including VGG-based, SENet, CBAM, Self-Attention, and Dual-Attention architectures. Pre-trained MobileNet models with Self-Attention and SENet are also included. A web application allows for easy demonstration of the model's capabilities.
A deep learning–based computer vision training pipeline for car damage detection using a Co-DETR learner enhanced with CBAM Attention, Hybrid Loss, and Albumentations. Trains on Colab to identify and localize car body defects such as scratches, dents, and rust. Includes end-to-end model training and quantitative evaluation.
This projected explored the effect of introducing channel and spatial attention mechanisms, namely SEN-Net, ECA-Net, and CBAM to existing CNN vision-based models such as VGGNet, ResNet, and ResNetV2 to perform the Facial Emotion Recognition task.
Developed a deep novel coupled profile to frontal face recognition network incorporating pose as an auxiliary information via attention mechanism (i.e., implemented a pose attention module).