Semi Supervised Classification Simclr

🚀 Semi-supervised image classification using contrastive pretraining with SimCLR

This project presents a simple image classification model trained with semi - supervised image classification using contrastive pretraining with SimCLR, aiming to solve the problem of image classification with partial labeled data and improve cost - efficiency.

🚀 Quick Start

There is no specific quick - start content provided in the original README.

✨ Features

• This is a semi - supervised image classification model trained using contrastive pretraining with SimCLR.
• The model is trained on the STL - 10 dataset which contains ten classes for image classification.

📦 Installation

There is no installation step provided in the original README.

📚 Documentation

🔍 Description

This is a simple image classification model trained with Semi - supervised image classification using contrastive pretraining with SimCLR. The training procedure was done as seen in the example on keras.io by András Béres.

The model was trained on STL - 10, which includes ten classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck.

📊 Metrics

There is a public W&B dashboard available here which illustrates the difference in different metrics such as accuracy of a baseline supervised trained model, a purely unsupervised model (pretrain) and the supervised finetuned model based on the unsupervised.

📖 Background

(by András Béres on keras.io ) Semi - supervised learning is a machine learning paradigm that deals with partially labeled datasets. When applying deep learning in the real world, one usually has to gather a large dataset to make it work well. However, while the cost of labeling scales linearly with the dataset size (labeling each example takes a constant time), model performance only scales sublinearly with it. This means that labeling more and more samples becomes less and less cost - efficient, while gathering unlabeled data is generally cheap, as it is usually readily available in large quantities.

Semi - supervised learning offers to solve this problem by only requiring a partially labeled dataset, and by being label - efficient by utilizing the unlabeled examples for learning as well.

In this example, I pretrained an encoder with contrastive learning on the STL - 10 semi - supervised dataset using no labels at all, and then fine - tuned it using only its labeled subset.

📄 License

The project uses the apache - 2.0 license.