Detecting Diabetic Retinopathy using Deep learning algorithm - Convolution neural network (Resnet-152) using PyTorch + GUI + SMS notification

Car Model Recognition project

This is an implementation of ResNet-50/101/152.

Building ResNet152 Model for Image Classification with Small Dataset (95% accuracy) | Tensorflow 2.0

This repo includes classifier trained to distinct 7 type of skin lesions

Deep Neural network using CNN pre-trained model to visually diagnose between 3 types of skin lesions

Pretrained on ImageNet ResNet-152 model in Keras

This repository is used to create Machine Learning models. Building three kinds of models that include covid detection, fruit and vegetable nutrition content, and general disease detection.

Resnet-18、Resnet-34 and Resnet-50 etc. caffe train prototxt files.

Code repository for training multi-label classification models on the CheXpert Chest X-ray dataset.

This is an implementation of the paper "Show and Tell: A Neural Image Caption Generator".

My PyTorch implementation of CNNs. All networks in this repository are using CIFAR-100 dataset for training.

Live Image Classification

Simple implementation of search for visually similar images using deep learning and vector search. It's based on pretrained ImageNet weights so it doesnt require any additional training

A dog breed classifier made using Convolutional Neural Networks and Transfer Learning, as a part of the Udacity's Machine Learning Engineer Nanodegree.

Developed a and Cover Classification system using Satellite Image Processing with the help of Remote Sensing images. The system can classify between Forest land, Agricultural of Paddy fields nd Urban areas from a given Dataset. All the step by step procedure has been done and executed in the Jupyter notebook. The system can also clasify the Land…

Multiple Imagenet frameworks used for "dogs vs cats"

DeepLearning Benchmark for multi-harware & multi-framework

Implementation of ResNet series Algorithm

Code repository for Tactode tiles recognition using: (i) machine learning with HOG&SVM, (ii) deep learning with some neural networks like VGG16/19, ResNet152, MobileNetV2, YOLOv4 and SSD, (iii) matching of handcrafted features with SIFT, SURF, ORB and BRISK, and (iv) template matching.