TensorFlow (Keras) implementation of MobileNetV3 and its segmentation head
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Updated
Jul 6, 2023 - Python
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
Complete introduction to deep learning with various architechtures. Maths involved is also included. Code samples for building architechtures is included using keras. This repo also includes implementation of Logical functions AND, OR, XOR.
Different CNN Architectures for Medical Image Segmentation task
Malaria-Detection-Using-CNN
This research introduces PotatoGANs, a novel data augmentation technique using GANs to generate synthetic potato disease images, improving model generalization in agricultural disease segmentation
Deliverables relating to the Advanced Computer Vision for AI University Unit
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.
CentraleSupélec/OpenClassrooms Data Scientist 2024-2025 - Projet 8 (veille technologique seule)
Keras implementation of influential CNN architectures.
Implementation of Dilated VGG Networks for Image Classification task
Driver's Drowsiness Prediction Using CNN Architechtures
The goal of this project is to study and implement VGG19 based classification model for Flowers' dataset.
Advanced concepts in computer vision, covering topics such as image classification, object detection, and transfer learning techniques for enhanced model performance and accuracy.
[Unmaintained] Notes and implementations of some papers that I had read 📄
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