Active Learning - Uncertainty Sampling
Pytorch implementation of "A New Active Labeling Method for Deep Learning. IJCNN 2014".
Environments
NVIDIA pytorch docker [ link ]
docker pull nvcr.io/nvidia/pytorch:22.12-py3
requirements.txt
accelerate
wandb
torchvision
Methods
./query_strategies
- Least Confidence
- Margin Sampling
- Entropy
Results
Experiment Setting
- Model: ResNet18
- Batch Size: 128
- Optimizer: SGD
- Learning Rate: 0.1
- Learning Rate Scheduler: Cosine Annealing with Warm-up
Active Learning
- The Number of Initial Labeled Images: 5,000
- The Number of Query Images: 500
- The Number of Iteration: 20