To develop a computer aided diagnosis tool to detect the presence of diabetic retinopathy and classify whether it is a normal diabetic retinopathy or an abnormal diabetic retinopathy.
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Updated
May 10, 2024 - Jupyter Notebook
To develop a computer aided diagnosis tool to detect the presence of diabetic retinopathy and classify whether it is a normal diabetic retinopathy or an abnormal diabetic retinopathy.
Wondering of which breed is that streat dog. Model to predict the breed of a street dog given its image. Usses RESNET for predoction.
Udacity Deep Learning Nanodegree project
This project focuses on detecting diseases in cotton plants using machine learning techniques. Early detection of diseases in cotton plants can help farmers take preventive measures and ensure better crop yields. The project uses a Convolutional Neural Network (CNN) based on ResNet152 architecture for image classification.
Deep-Learning Classification Project
exploring image captioning
Fall 2021 Introduction to Deep Learning - Homework 2 Part 2 (face classification, face verification)
Tensorflow 2 implementations of ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152 from Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun (2015)
This repository hosts all the scripts used in the implementation of bird detection models. We are using Convolutional Neural Networks(CNN)'s Faster R-CNN, Single Shot Detector(SSD), and YOLOv3 meta-architectures while utilizing ResNet-101, MobileNet, Inception ResNet v2 and VGG-16 feature extraction Networks (backbone network).
Cotton leaf disease prediction using ResNet-152v2 deep residual network architecture.
ResNet Comparison for Garbage Image Classification with PyTorch - models trained on GPU, then pickled for analysis on CPU
Automatic Diagnosis of COVID-19 using CT Scan
Dog breed classifier using Resnet150 and Pytorch
Implementation of resnet.
Classification CNN models
Simple object detection from camera using ResNet152
An OCR to detect and identify text in Indian Regional Languages
A Deep Learning model trained on Cifar-10 dataset, with the help of fastai library and ResNet-152 architecture, which achieve 86.22% accuracy.
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