Development and implementation of various image classifiers using deep learning.

Protein localization in cell microscopy images Kaggle competition

The binary classification problem focused on first IEEE Image forensics challenge-phase 1, to predict the given image is pristine or manipulated/edited/fake. Comparing CNN & Transfer Learning models for the problem and boosting the performance by feature extraction

Using Neural Networks on Chest Xrays to find abnormal and normal xrays to diagnose for Pulmonary Tuberculosis

Scaling Object Detection by Transferring Classification Weights

Retrain a pre-trained Neural Network to recognize Images

Deep Learning for Image Classification

Deploy AutoML models for image classification on AWS Sagemaker with AutoGluon

Tensorflow 版本的图片鉴黄。not suitable/safe for work (NSFW) images detection using Tensorflow

Invert and perturb GAN images for test-time ensembling

In this case study, I combine several subcategories of data not based on item color but based on item type. These types will be classified into types of clothing and shoes. The result of merging these subcategories is that there are several main categories which are divided into 5 categories, namely Dress, Pants, Shirt, Shoes, Shorts. I'm trying…

Some of my Machine Learning projects

It is an image classification problem involving the usage of various custom and pretrained keras models on land classification dataset containing 21 classes.

boostcamp AI Tech: 이미지 분류 대회

Web Based Image Recognition System in Python Flask

Federated multicloud kubernetes cluster deployment using Infrastructure as Code for Image Classification

Project code for Udacity's AI Programming with Python Nanodegree program. In this project, code was developed for an image classifier built with PyTorch, then converted into command line applications: train.py, predict.py.The image classifier recognizes different species of flowers.

[CVPR 2019] SketchGAN: Joint Sketch Completion and Recognition with Generative Adversarial Network

Implementations of various classifiers on MNIST dataset. Both traditional machine learning and deep learning methods are included.

Deep learning basics