A microservice that uses keras and tensorflow to classify images
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Updated
Dec 11, 2018 - Python
A microservice that uses keras and tensorflow to classify images
Image Classifier using common DL Frameworks
Built image classification deep learning architectures - AlexNet, VGG16, and ResNet using transfer learning and fine-tuning in PyTorch. Final model accuracies achieved are AlexNet-81.2%, VGGNet-85.6%, ResNet-84.7% on 10K test images.
A command line application that implements an image classifier with PyTorch. Part 2 of final project for Udacity's AI Programming with Python Nanodegree program.
Pre-trained Image Classifier, part of the Udacity AI Programming with Python Nanodegree
Image Classifier built using Python, OpenCV. Using ORB for feature detection and knn matcher for matching the features.
This project aims, through machine learning techniques, at creation a model for image classifications.
Used PCA for dimension reduction of a 25x25 animal image dataset. After the feature extraction step, a KNN classifier to distinguish the images in a 3D plane (3PC extraction). PCA and KNN are implemented from scratch. Matplot is used for 3D visualization.
Tensorflow binary image classifier based on ResNet50
Deep Neural Network Classifier to recognise images based on custom training classes.
This repository aims to provide a primitive tool to finetune state-of-the-art models with PyTorch implementation, similar to Nvidia TAO but with more flexibility in augmentation and models.
Retrain a pre-trained Neural Network to recognize Images
Image classifier based on Pytorch
Classification of different types of Rashes using TensorFLow
Keras image classifier provided as web api
A convolutional neural network from scratch to classify images (demo available)
This repository is all about classifying images in different categories in the Python Programming Language with TensorFlow.
A Convolutional Neural Network (CNN) based Image Classifier Using Machine Learning
A neural-network based image classifier that quantifies its uncertainty using Bayesian methods, as described in Kendall and Gal (2017)
Select images and classify them into user defined categories.
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