Tagging interface w/ transfer learning, linearized active search and uncertainty sampling:
| Transfer learning | https://github.com/bkj/tdesc |
| Linearized active search (LAS) | https://github.com/bkj/simple_las |
| Uncertainty sampling | https://github.com/bkj/libact |
Under active development -- some things are broken or don't have sensible APIs exposed.
cd $TARGET_DIR
mkdir -p ./{data,results}
# expect set of images to be in `imgs` directory
# Featurize images
find ./imgs/ -type f | python -m tdesc --model vgg16 --crow > .crow
# Prep + reformat images
python $TAGLESS_ROOT/tagless/prep.py --inpath .crow ./data/crow
# Run server
python -m tagless --outpath ./results/my-labels --crow ./data/crow
# Connect to localhost:5000 + start tagging
Uncertainty sampling computes the score for each unlabeled image at each iteration. ATM we're using a linear SVM, so the runtime of this step increases linearly w/ the size of the corpus. On my machine, predicting on ~350K images takes ~2.5s, which is unacceptably slow. Thus, for big corpora, we may want to fall back to some kind of approximate matrix-vector product. That'll take a little bit of thought thought. For now I'll recommend running on a subset of the data.
Idea: Feature vectors are normalized relus -- so norm=1 and all positive entries. Could maybe do uncertainty sampling via faiss by using vector orthogonal to SVM feature vector and take the largest/smallest entries. Have to check my work on that one though.
This has been tested on Ubuntu 16.04 w/ Python 2.7 (via Anaconda)