Implementation of DCGAN for the generation of flowers.
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Updated
Nov 6, 2020 - Jupyter Notebook
Implementation of DCGAN for the generation of flowers.
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…
This project showcases a cats vs dogs image classification model using image augmentation and Keras. It employs deep learning and convolutional neural networks (CNNs) to accurately classify images of cats and dogs.
TNNLS 2024 submission. VerDisGAN and HorDisGAN which control the variation degrees for generated samples
Image augmentation extension for the image-dataset-converter library.
Image augmentation for machine learning experiments.
Deep learning network to visually recognize traffic signs, trained on the German Traffic Sign Benchmark (GTSB).
Academic Machine Learning (6 months) Sessional Project
This repository contains code and dataset for a multiclass image classification model to detect monkeypox using the ResNet50 architecture. The project focuses on classifying skin lesion images into different categories related to monkeypox, achieving 100% accuracy.
Classification of Sober and Intoxicated Faces using Image Analysis
Convolutional Neural Network (CNN) with image augmentation project to classify hand image of rock, paper, and scissors
Submission - Last Project : Image Classification - Machine Learning for Beginners - Machine Learning Path - DICODING
This project is to build a classification model by transfer learning.
This project is about how to apply image data augmentation in Keras. And focused on using the ImageDataGenerator class from Keras’ image preprocessing package, and gives insight of a variety of options available in this class for data augmentation and data normalization.
Adopted a convolutional neural network for COVID-19 testing. Examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models.
Create unique stickers based on textual prompts.
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