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[aidl] Image Captioning

Documentation

Installation

git clone https://github.com/joel-mb/aidl-image-captioning
cd aidl-image-captioning
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt

Usage

Training

All the training logic is inside the train.py file. The following arguments may be selected:

Option Description
--session-name <STRING> name of the session
--data-root <PATH> path to Flickr8k data folder (default: data/flickr8k)
--num-epochs <INT> number of epochs (default: 10)
--batch-size <INT> batch size (default: 32)
--learning-rate <FLOAT> learning rate (default: 1e-3)
--num-workers <INT> number of workers used in the data loader (default: 4)
--encoder-type <CHOICE> encoder type (choices: [resnet101, senet154], default: resnet101)
--attention-type <CHOICE> attention type of the decoder (choices: [none, additive], default: additive)
--vocab-size <INT> minimum frequency of a word to be added in the vocab (default: 1)
--max-seq-length <INT> maximum sequence length (default: 25)
--encoder-size <INT> encoder size (default: 64)
--hidden-size <INT> hidden size (default: 256)
--embedding-size <INT> embedding size (default: 128)
--attention-size <INT> attention size (default: 64)
--no-save-model do not save trained model. The model is saved in the models folder
--log-interval <INT> logging step with tensorboard (per batch) (default: 25)
--save-checkpoints save checkpoints. Checkpoints are saved in the checkpoints folder
--overfitting use the overfitting dataset (data/flickr8k_overfitting)

Example of usage:

python train.py --session-name test1 --num-epochs 50 --attention-type none

Inference

All the inference logic is inside the inference.py file. The following arguments may be selected:

  • Positional arguments
Option Description
model_name <STRING> name of the model to be used (session_name option of the training phase)
image_path <PATH> path to the image to predict the caption
  • Optional arguments
Option Description
--data-root <PATH> path to Flickr8k data folder (default: data/flickr8k)
--max-seq-length <INT> maximum sequence length (default: 25)

Example of usage:

python inference.py test1 data/flickr8k_overfitting/Flickr8k_Dataset/12830823_87d2654e31.jpg