AL-SEG: Active Learning for Semantic Segmentation

AL-SEG is an open-source active learning tool for semantic segmentation based on PyTorch. It is built on top of mmsegmentation, which provides extensive support for various datasets, pre-trained backbones, and state-of-the-art segmentation methods right out of the box.

This repository contains the official implementation of the paper: Conformal Risk Controlled Active Learning (CRC-AL), which has been recently submitted. The source code of this project will be publicly available once the paper is accepted.

In addition to our proposed CRC-AL approach, this project also includes Python implementations of several active learning algorithms adapted for semantic segmentation outputs:

Methods	References
Random Sampling	-
Entropy Sampling	D. D. Lewis, J. Catlett, Heterogeneous uncertainty sampling for supervised learning, in: Machine Learning Proceedings, 1994.
Margin Sampling	D. Roth, K. Small, Margin-based active learning for structured output spaces, in: Machine Learning: ECML, 2006.
Least Confidence	A. J. Joshi, F. Porikli, N. Papanikolopoulos, Multi-class active learning for image classification, in: IEEE CVPR, 2009.
Stochastic Batch Sampling	M. Gaillochet, C. Desrosiers, H. Lombaert, Active learning for medical image segmentation with stochastic batches, Medical Image Analysis, 2023.
Core-Set Selection	O. Sener, S. Savarese, Active learning for convolutional neural networks: A core-set approach, in: ICLR, 2018.
Contextual Diversity	S. Agarwal, H. Arora, S. Anand, C. Arora, Contextual diversity for active learning, in: Computer Vision – ECCV, 2020.
BADGE	J. T. Ash, C. Zhang, A. Krishnamurthy, J. Langford, A. Agarwal, Deep batch active learning by diverse, uncertain gradient lower bounds, CoRR, 2019.

Setup Environment

Setup with CUDA (Nvidia)

This section applies if you have an Nvidia GPU and want to leverage CUDA for hardware acceleration.

conda env create -f install/env_cu116.yaml
conda activate AL-SEG
pip install mmengine==0.8.5
pip install mmcv==2.0.0rc4 -f https://download.openmmlab.com/mmcv/dist/cu116/torch1.13/index.html
pip install mmsegmentation==1.1.2

Setup with MPS (Apple Silicon)

This section is relevant for users on Apple Silicon (e.g., M1 or M2 Macs) who want to utilize Metal Performance Shaders (MPS) acceleration.

conda env create -f install/env_mps.yaml
conda activate AL-SEG
pip install mmengine==0.8.5
pip install mmcv==2.0.0rc4 -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.13/index.html
pip install mmsegmentation==1.1.2

Verify Device

To confirm that your CUDA or MPS setup is functioning correctly and to run basic performance tests, execute:

python scripts/test_device.py