Segmentation using DroneDeploy Machine Learning Benchmark
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Updated
Apr 30, 2020 - Python
Segmentation using DroneDeploy Machine Learning Benchmark
AI Capstone Project Course using Pytorch
Project for fast.ai - Practical Deep Learning for Coders - Lesson 2.
A pre-trained image classifier to classify dog breeds. Udacity Nanodegree project-1.
Fine-tuning Resnet18 model to predict if the patient has Pneumonia (COVID19) with the help of x-ray
Detection of COVID-19 Pneumonia from chest X-Ray images using ResNet-18.
Building a Garbage classifier that classifier trash into 6 categories: Glass, Metal, Cardboard, Paper, Plastic and Trash.
A classifier for 120 dogs classified at Stanford Dogs Dataset, using the Pytorch framework and using custom Resnet for neural network learning
Development an Deep Learning model to detect COVID-19 in chest X-ray scans.
Automatic Diagnosis of COVID-19 using CT Scan
Classification of images based on facial features using ResNet18 as base framework integrated with Non-local neural networks
Implementing Transfer Learning using Resnet18. Training and testing Resnet18 on Even numbered Classes in CIFAR10 Dataset.
Several deep learning methods has applied for example datasets
Using transfer learning to train a ResNet18 model to identify the age of a brain from its MRI scan.
ResNet Comparison for Garbage Image Classification with PyTorch - models trained on GPU, then pickled for analysis on CPU
Course Project - Advanced Topics in Machine Learning - Autumn Semester 2023 - Indian Institute of Technology Bombay
Fall 2021 Introduction to Deep Learning - Homework 2 Part 2 (face classification, face verification)
The aim of this project is to implement an image classifier based on convolu- tional neural networks. Starting by implementing a simple shallow network and then refining it until a pre-trained ResNet18 is implemented, showing at each step how the accuracy of the model improves. The provided dataset (from [Lazebnik et al., 2006]) contains 15 cate…
By comparing the performance of 24 custom trained models with various configurations, I aim to identify the most effective model architecture and configuration for accurate gel electrophoresis image classification.
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