cnn-architectures

TensorFlow (Keras) implementation of MobileNetV3 and its segmentation head

Spatial and Channel Attention in CNN Architectures for Image Classification task

This folder contains my reading notes on different topics in Machine Learning.

CNN Architectures(with and without SPP Layer) for Image Classification task

Different CNN Architectures for Image Classification task

Repo for the paper "Deep Multimodal Transfer-Learned Regression in Data-Poor Domains"

Implementation of Dilated Residual Networks for Image Classification task

Architecture-level study of how early CNN downsampling choices affect bias, generalization, and CPU inference behavior under constrained settings.

Different CNN Architectures for Medical Image Segmentation task

This research introduces PotatoGANs, a novel data augmentation technique using GANs to generate synthetic potato disease images, improving model generalization in agricultural disease segmentation

Malaria-Detection-Using-CNN

Deliverables relating to the Advanced Computer Vision for AI University Unit

Implementation of Dilated VGG Networks for Image Classification task

Advanced concepts in computer vision, covering topics such as image classification, object detection, and transfer learning techniques for enhanced model performance and accuracy.

CentraleSupélec/OpenClassrooms Data Scientist 2024-2025 - Projet 8 (veille technologique seule)

This repository contains coding files that will help us determine which CNN model architecture performs best at classifying a dog and its various breeds. This project was completed as part of AI Programming with Python Nanodegree program (Udacity).

This was just a project regarding the basics of deep learning. And it has covered most of the basics needed to get an idea regarding hyperparameter tuning, different CNN architecture etc.

Keras implementation of influential CNN architectures.

Deep learning feasibility study for automated clove quality classification using CNN architectures on a novel Zanzibar dataset. AI for East Africa Conference (AI4EAC) 2026, Kigali, Rwanda.

The goal of this project is to study and implement VGG19 based classification model for Flowers' dataset.