Introduction of semi-supervised-classification-simclr
Model Details of semi-supervised-classification-simclr
Semi-supervised image classification using contrastive pretraining with SimCLR
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.
Runs of keras-io semi-supervised-classification-simclr on huggingface.co
23
Total runs
0
24-hour runs
0
3-day runs
12
7-day runs
-5
30-day runs
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