Implementing Transfer Learning for custom data using Resnet-18 in Pytorch.

Dog vs Cat Classification model trained over MobileNetv2. The model is trained on 2000 images and gives an accuracy of 98.75%

Бинарная классификация рентгеновских снимков грудной клетки. Определение наличия пневмонии у пациентов при помощи различных CNN архитектур. Использование метода Transfer Learning

In this repository, we leverage the power of few-shot learning combined with a transfer learning approach to tackle the task of object detection.

A Github repository containing code for a study on Covid-19 detection through Chest X-rays using deep learning and custom algorithms. Achieved an accuracy range of 82-88 percent by analyzing pneumonia traces.

ML model to recognize cats and dogs using transfer learning

Transfer learning methods using Tensorflow

A practical guide on using transfer learning for binary classification tasks using TensorFlow

This project is an image classification project based on a transfer learning approach using with MobileNetV2 architecture.

Using LIME and Grad-CAM techniques to explain the results achieved by various image transfer learning techniques

Brain Tumor MRI images have been classified into 4 classes by using Transfer Learning Models.

This deep learning model(CNN) uses Transfer learning by Feature Extraction and Fine Tuning in order to make multiclass-classification between COVID-19, Pneumonia and Healthy images.

This repo gives an introduction to transfer learning using a pre-trained model Inception.

Code for the project "Exploring transferability and model agnostic meta learning across NLP Tasks". CS330 Deep Multi-Task and Meta Learning, Stanford University.

Pneumonia identification from chest x-ray images using deep learning algorithms Used transfer learning techniques to develop an artificial intelligence system.

Dog Breed Identification using Transfer Learning implemented in TensorFlow & PyTorch

This project is an image classification task of 450 bird species using the MobileNetV2 architecture.

Determine whether the Brain MRI image has a tumour. It also segments the brain image.

A comparison between Transfer Learning and custom Convolutional Network to classify images.