Popular GitHub Repositories for Transferlearning

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Transfer Learning for Anime Characters Recognition

A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework

To Detect and Classify Brain Tumors using CNN and ANN as an asset of Deep Learning and to examine the position of the tumor.

Automatic Annotation tool for labelling images in bulk with their corresponding bounding box annotations.

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

Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecasting: CS229 Final Project

Android Application with Tensorflow Backend for Plant Image Classification

A machine learning-based project aimed at detecting and identifying plant diseases from images of affected plants. Plant Disease Detection Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Image classification using CNN and transfer learning from pre-trained models for better performance.

Facial Keypoint Detection using PyTorch and a modified VGG16 architecture. This project identifies 68 facial landmarks—covering eyes, nose, mouth, jawline, and eyebrows—on human faces. The model is trained on normalized keypoints with adaptive pooling, custom dataset handling, and transfer learning.

This project is a custom-trained image classification model designed to detect and classify 10 types of insect pests commonly found on tea leaves. The entire pipeline—from dataset creation to prediction—is built and documented by Birendra, a Computer Science student exploring practical applications of deep learning in agriculture.

This project builds a Convolutional Neural Network (CNN) to classify landmarks from images. It includes developing a CNN from scratch and enhancing performance with transfer learning (achieving 75% accuracy using ResNet18).