This repo contains tutorials covering image classification using PyTorch 1.7, torchvision 0.8, matplotlib 3.3 and scikit-learn 0.24, with Python 3.8.

A simple demo of image classification using pytorch. Here, we use a custom dataset containing 43956 images belonging to 11 classes for training(and validation). Also, we compare three different approaches for training viz. training from scratch, finetuning the convnet and convnet as a feature extractor, with the help of pretrained pytorch models.

Learning and Building Image Classification Models using PyTorch.

Classifies an image as containing either a dog or a cat (using Kaggle's public dataset), but could easily be extended to other image classification problems.

November 15, 2018 - Source Code of Project Image Classification with PyTorch :fire: - rowhitswami/Image-Classification-with-PyTorch

July 20, 2022 - PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet - hysts/pytorch_image_classification

image classification using pytorch-project-template, train a model of classification easily by modifying a json configuration - frotms/image_classification_pytorch

The objective of this project is to build an application that can be trained on any set of labeled images, using tools in PyTorch. The network instance in the Jupyter notebook is trained on flower images—102 Category Flower Dataset—and provides predictions via a command line application. Below is an example of the type of images included in the dataset: The project in the Jupyter notebook is broken down into three main steps: ... flower-classifier-PyTorch.ipynb: Jupyter notebook including main Python code used in developing the tool.

Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision. - pytorch-image-classification/1_mlp.ipynb at master · bentrevett/pytorch-image-classification

Image Classification system built with PyTorch using Deep Learning concepts in order to recognize different species of flowers. - haardikdharma10/Image-Classifier-using-PyTorch

This is a simple convolutional neural network to classify images with PyTorch. The model has been designed to classify images using the Intel Image Classification dataset, which can be found in Kaggle here.

use pytorch to do image classfiication tasks . Contribute to spytensor/pytorch-image-classification development by creating an account on GitHub.

Image Classification architectures implemented in PyTorch.

Simple image classification for a custom dataset based on PyTorch Lightning & timm.

An image classifier built on PyTorch and enabled for both Jupyter Notebooks and Python - elinorwahl/pytorch-image-classifier

:dart: Deep Learning Framework for Image Classification & Regression in Pytorch for Fast Experiments - MIC-DKFZ/image_classification

A general, feasible and extensible framework for 2D image classification. Easy to configure (model, hyperparameters) Training progress monitoring and visualization · Weighted sampling / weighted loss / kappa loss / focal loss for imbalance ...

The goal of this project was to build and train an image classifier that is able to recognize different species of flowers. We relied on the so-called transfer learning approach - using pre-trained deep convolutional neural networks (e.g. VGG, DenseNet, AlexNet imported from Pytorch torchvision) as fixed feature extractors and combining them with custom fully-connected classifiers for solving the given task.

This repository implements the following algorithms for image classification using the PyTorch framework:

As an example, we use Kaggle dataset for classification of image of cats and dogs("https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip"). The example download and process the data into training and validation sets. The model for this example is set up in "catvsdog_example.py". For running, it requires to install the next libraries: -PyTorch -torchvision -tqdm -zipfile -cv2 You can create other model for image classification, you only need to provide the dataset in the variable "parameters['dataset']"