Activizer

Interface for Active Learning

About Active Learning

Active learning is the process by which your model chooses the training data it will learn the most from, with the idea being that your model will predict better on your test set with less data if it’s encouraged to pick the samples it wants to learn from.

Interface for Active Learning supports Image dataset where the user can upload data in Zip or RAR format. It supports 3 classifiers and 7 query strategies.

Classifiers

• KNN Classifier

• Random Forest Classifier

• Decision Tree Classifier

Query By Committee

• Uncertainty Sampling

• Random Sampling

• Entropy Sampling

• Query By Committee(Uncertainty Sampling)

• Query By Committee(Vote Entropy Sampling)

• Query By Committee(Max Disagreement Sampling)

• Query By Committee(Consensus Entropy Sampling)

This project is implemented with the Active Learning package modAL

How to Run

• Select the Classifier Algorithm, the Query Strategy and give the number of samples you wish to label. Then select the training / testing dataset in Zip format

• For each iteration an image will be shown and a dropdown to label that image. Below them will be shown a graph with current accuracy.

• After all the iterations are over, Final accuracy with graph will be shown

• The user can see the images along with the labels provided by the algorithm selected during training. The trained model can be downloaded in pickle format (.pkl file) and can be used for prediction by unpickling it.

• The Interface can be used for prediction by uploading the validation dataset in Zip format.

• The result will be shown in a table consisting of image name and label predicted by the model.