CEAL

Pytorch implementation of Cost-Effective Active Learning for Deep Image Classification paper

Difference from the original paper

1. Image input : 224 x 224 instead of 227 x 227

2. Learning rate: 0.001 for all layers.

3. Freeze all the layers except the last one.

4. Experiments done only on Caltech256.

5. No comparison to other methods.

How to use the code

1. Install conda environment conda env create -f environment.yml
2. Download Caltech256 from caltech256
3. Run scripts divide_data.sh to divide data into test and train
4. main_program run_ceal/ceal_learning_algorithm.py

References:

Some code is modified from this repo