Implementing Transfer Learning for custom data using Resnet-18 in Pytorch.
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Updated
Dec 5, 2022 - Jupyter Notebook
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%
This repo consists a Python Notebook file where I have performed transfer learning using Keras Xception Transformer.
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.
Transfer learning methods using Tensorflow
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
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.
A Comparative study on the performance of multiple transfer learning model in deepfake detection
The project consists of applying the Transfer Learning method to a Deep Learning network in the Python language in the COLAB environment.
A deep learning project which explores the capabilities of different models trained with a very small dataset of x-rays
Deep Learning project where we had to build a model which classifies, by looking at an unseen human retina, if the eye is normal or has one of 3 diseases. Authors: André Cunha | Catarina Duarte | Cláudia Rocha | Dinis Melo | Susana Dias | Lisbon, Portugal | April 2023
Repository containing the project for the course on Computational Intelligence and Deep Learning at the University of Pisa.
A Deep Learning Model to classify CIFAR-10 dataset using 1.) Convolutional Neural Network 2.) Transfer Learning using ResNet-18 in PyTorch.
Image Classification
Transfer Learning based Blood Cell Type Image Classification.
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