Light Bulb is an labeling tool built with state of the art active learning and semi supervised learning techniques. Currently supports text classification and image classification.

• Getting Started
  • Mac OSX
• Usage
  • Configuration
  • Example Text Classification
  • Example Image Classification
• How It Works
  • Semi Supervised Text
  • Semi Supervised Image
  • Active Learning
• Coming Soon

brew install yarn
git clone https://github.com/czhu12/labelling-tool && cd labelling-tool
make

Heres an example configuration:

task:
  title: What kind of animal is this?
  description: Select the type of animal you see, if there is none, select "Skip"
dataset:
  directory: dataset/image_classification/
  data_type: images
  judgements_file: outputs/image_multiclass_classification/labels.csv
label:
  type: classification
  classes:
    - Dog
    - Cat
    - Giraffe
    - Dolphin
    - Skip
model:
  directory: outputs/image_multiclass_classification/models/
user: chris

task:
  title: What kind of animal is this?
  description: Select the type of animal you see, if there is none, select "Skip"

dataset:
  directory: dataset/image_classification/
  data_type: images
  judgements_file: outputs/image_multiclass_classification/labels.csv

judgements_file defines the file that the labels are saved in.

data_type defines what type of model is used. Valid options are images and text

label:
  type: classification
  classes:
    - Dog
    - Cat
    - Giraffe
    - Dolphin

type defines the type of label, options are classification and binary.

model:
  directory: outputs/image_multiclass_classification/models/

directory defines where the trained model is saved.

user: chris

user defines who the person labeling is, which may be useful when the label's are used.

To run the text classification demo:

make run config/text_multiclass_classification.yml

To run the Image classification demo:

make run config/image_multiclass_classification.yml

Most deep learning tasks can be framed as a encoder - decoder architecture. For example, text classification can be framed as an LSTM encoder that outputs into a logistic regression decoder. Object detection can be framed as a ResNet encoder with a regression decoder. All models in Light Bulb are framed as an encoder - decoder architecture, and the encoder are pre-trained on an external dataset (Image Net for images, and Wikitext-103 for text), and then fine-tuned on the target dataset.

Light Bulb's text encoder is a pretrained language model on wikitext-103 (inspired by ULMFiT), with a vocab limited to the most frequent 100k words in the corpus. The model is fine-tuned on the target dataset as a language model.

Light Bulb uses Squeeze Net pretrained on the ImageNet dataset to encode image data. The encoder is fine-tuned on the target dataset that is given to be labeled as an auto-encoder. Standard image augmentation techniques are used to expand the labeled training set.

Light Bulb will train a model as you provide training data through labeling. Light Bulb will sample items to be labeled by scoring the unlabeled items and sample the highest entropy items.

• Sequence Tagging
• Object Detection
• Sequence to Sequence Modeling
• Dockerize Application