PrateekMunjal/TorchAL
Official implementation of our paper: Towards Robust and Reproducible Active Learning using Neural Networks, accepted at CVPR 2022.
This project helps machine learning practitioners efficiently categorize large datasets by selecting the most informative data points for human annotation. It takes a large pool of unlabeled data and, through an iterative process, identifies a smaller, critical subset for manual labeling, which then trains a more accurate classification model. Data scientists and ML engineers, especially in fields with high labeling costs, would benefit from this to reduce annotation effort.
No commits in the last 6 months. Available on PyPI.
Use this if you need to train robust image classification models with deep neural networks but face high costs or time constraints for manually labeling your entire dataset.
Not ideal if your dataset is small enough to be fully labeled without significant effort, or if you are not working with image classification tasks using deep neural networks.
Stars
69
Forks
7
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 16, 2023
Commits (30d)
0
Dependencies
12
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