Skip to content

Latest commit

 

History

History
34 lines (27 loc) · 3.1 KB

README.md

File metadata and controls

34 lines (27 loc) · 3.1 KB
Raw

What is this about?

This repository has been created to enhance collaboration and document the results of the project in the lecture "Neural Networks and Sequence to Sequence Learning". The aim is to implement image captioning using primarily the pytorch machine translation framework JoeyNMT. As baseline model we implement the approach of Xu et al. (2015).

Visit our Demo

You can let our best-performing model generate captions for any image here.

Website Demo

Model Architecture

Just like Xu et al. (2015) we use an encoder network to retrieve features from images. The feature vector is then used to initialize a LSTM decoder, which unrolls a generated caption. For each step, an attention mechanism is applied on the feature vector. The attention mechanism is illustrated below using a real example and shows how our implementation attends to different areas of the image during unrolling.
Unrolling

Usage

Training

  1. Make sure to install dependencies listed in requirements.txt.
  2. In order to work with our implementation, load the Flickr8k dataset from https://github.com/goodwillyoga/Flickr8k_dataset.
  3. Place the files Flickr8k.token.txt, Flickr8k.trainImages.txt, Flickr8k.devImages.txt, Flickr8k.testImages.txt, the ExpertAnnotations.txt file as well as the folder containing all images in a data folder in project root.
  4. Adapt the location and name of the above mentioned files in train.py, if necessary.
  5. Create a .yaml file in the param folder in project root. You should give this file a meaningful name. Define in the file all parameters of the experiment you want to execute. An example .yaml file with explanations can be found in the param folder of this repository.
  6. Start training: python train.py modelname. Set model_name to the name given to the .yaml file containing the desired training parameters.
  7. During training, the loss and BLEU score evaluations on the train data will be stored inside a runs folder, named accoring to the model name given before. These data points can easily be visualized using Tensorboard. The trained model is stored as a .pth file in the folder saved_models.

Evaluation on Test Set

  1. Make sure the .pth file of the model you want to evaluate exists in the saved_models folder.
  2. In eval.py, change the model_name to the name of a .yaml file containing the same entries as the trainining file and additionally the entry load_model, set to the path of the .pth file. Example: load_model: 'saved_models/best_model.pth'
  3. Start evaluating with python eval.py. Evaluation will be done using the test split and results will be put out to the console.

Best Model

Our best-performing model's weights can be downloaded here.