cbam-resnet

Implementation of Resnet-50 with and without CBAM in PyTorch v1.8. Implementation tested on Intel Image Classification dataset from https://www.kaggle.com/puneet6060/intel-image-classification.

CBAM(Convolutional Block Attention Module) implementation on TensowFlow2.0

This is a torchvision style CNN models collection based on pytorch.

A minimal Tensorflow2.0 implementation of Resnet on CIFAR10 dataset.

🩺 A comprehensive project leveraging YOLOv8 and Faster R-CNN for detecting thoracic abnormalities in chest X-rays. Optimized for medical diagnostics with CBAM attention, achieving precision and recall benchmarks. 🚀 Includes advanced preprocessing, class imbalance reduction, and detailed evaluation metrics.

AI & ROBOTICS HACKATHON 2022

Rice Leaf Disease Classification using Convolution Bottleneck Attention Model

Remote Sensing Change Detection

This repository contains a complete implementation of a plant disease classification system using a CBAM (Convolutional Block Attention Module) augmented ResNet18 architecture. The system is designed to accurately identify various plant diseases from images, leveraging attention mechanisms to focus on the most relevant features for diagnosis.

This repository implements a neural network that upscales low-resolution brightness temperature data from AMSR2 satellite observations by 2×, 4×, or 8×. The model uses spatial attention mechanisms and residual learning to reconstruct high-resolution thermal fields while preserving physical consistency. Achieves 40-41 dB PSNR on 2× upsampling.