👳🏾♂️ Comparing accuracies of different convolutional neural networks for the task of classifying Indians into North and South Indians
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Updated
Apr 13, 2021 - Jupyter Notebook
👳🏾♂️ Comparing accuracies of different convolutional neural networks for the task of classifying Indians into North and South Indians
Create a Convolutional Neural Network using TensorFlow.
This repository contains code and results for COVID-19 classification project by Deep Learning Spring 2020 course offered at Information Technology University, Lahore, Pakistan. This project is only for learning purposes and is not intended to be used for clinical purposes.
In this paper I introduce a new oral lesion database that I created by scraping Images from research papers and dentists online. Next, I showcase a novel method for generating NxN pixel samples from human lips by isolating the labial fissure via image segmentation induced via the Kmean algorithm. I experiment with a variety of convolutional neur…
Image Classification for a City Dog Show Project Goal Improving your programming skills using Python. In this project, I use a created image classifier to identify dog breeds. It is for one of the submission for the udacity nanodegree program
🎨 Automatic Image Colorization using TensorFlow based on Residual Encoder Network
CNN distinguishing between men and women by photo
Binary classification of COVID-19 and non-COVID-19 chest X-rays
Funciones utilles para PyTorch.
Pytorch implementations of each of the models described in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition" by Karen Simonyan, Andrew Zisserman
Machine Learning Practical - Coursework 2 Report: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during training. And exploring solutions using batch normalization and residual connections.
This repo implements a VGG model with SPP layer to make it receive images with different shapes, which is then used to classify images of culture relics and compare results under differnt images types - gray images with the same shape, gray images with different shapes, bgr images with the same shape, bgr images with different shapes
The repository contains a detailed analysis on implementing VGG19 and (plain-layered) VGG34 on the CIFAR-10 dataset with code, and an explanation on the distinctive difference between them. It serves the purpose of storing my HW2 for the ICRA training, HKU, 2020.
Demo on performing multiclass image classification using Convolutional Neural Network (CNN) in Tensorflow 2. Techniques such as earlystopping, batchnormalizing and dropout are explored to prevent overfitting
Using VGG to predict images with 10 classes
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