Official PyTorch implementation of "Visualizing the Decision-making Process in Deep Neural Decision Forest", CVPR 2019 Workshops on Explainable AI

Skin Lesion Analysis Towards Melanoma Detection

This repository consists of our Final Year Project. You can find everything starting from our code to all the resources in this repository

Classification of brain tumor in MR images using deep spatiospatial models.

This project related to road safety. We have to wear a helmet according to road safety rules when riding a motorcycle. Therefore, these rules are monitored by the police. Also, Due to the invention of high-quality cameras and the development of the state of art in AI, the surveillance system willing to use AI system to detect whether passengers …

Build a image classification python web app with Streamlit and PyTorch 🐱 🐶

Models for Computer Vision

GECCO is a lightweight image classifier based on single MLP and graph convolutional layers. We find that our model can achieve up to 16x better latency than other state-of-the-art models. The paper for our model can be found at https://arxiv.org/abs/2402.00564

Simple CNN is a library that can be used to train and infer CNN models by use of PyTorch and ONNX.

An AI-powered Image Classification Model that identifies objects in images. Supports multiple categories with high accuracy

Implementation of Distilling the Knowledge in a Neural Network https://arxiv.org/pdf/1503.02531.pdf

Image classification using satellite imagery.

For questions please contact: shohruh.maxmudov@mail.ru

Binary classifier of melanoma

Knowledge distillation pytorch lightning template for image classification task

This is a web app where you can draw a letter in the russian alphabet and the ML algorithm will predict the letter that you drew.

This repository contains the implementation of an image classification system using a Support Vector Machine (SVM). The goal of the project is to classify images of cats and dogs stored in two folders (train and test1). The classifier is trained on a labeled dataset and used to predict the category of unseen images from the test folder.

Implementation of Min-Max CNN for Image Classification in Pytorch

Image Classification using Keras as well as Tensorflow. Image Classification is a Machine Learning module that trains itself from an existing dataset of multiclass images and develops a model for future prediction of similar images not encountered during training. Developed using Convolutional Neural Network (CNN).

Detailed guide on how to train model using Python TensorFlow and the training data. Also has code to split, train, test and convert the .h5/.keras to .tflite