add code and README

README.md

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+---
+library_name: keras
+tags:
+- image-classification
+datasets:
+- STL-10
+license: apache-2.0
+---
+## 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 <a href='https://keras.io/examples/vision/semisupervised_simclr/' target='_blank'>**keras.io**</a>  by András Béres.
+
+The model was **trained on STL-10**, which includes ten classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck.
+
+
+## Background
+(by András Béres on <a href='https://keras.io/examples/vision/semisupervised_simclr/' target='_blank'>**keras.io**</a> )
+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.

keras_metadata.pb

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