Tensorflow/Keras implementation of an image captioning neural network, using CNN and RNN.
This is an unofficial implementation of the image captioning model proposed in the paper "Show and tell: A neural image caption generator.".
This implementation is a faithful reproduction of the technique proposed in the paper, where during training we provide the model with examples composed of an image, its caption (inputs) and the caption with words shifted one position to the left (ground-truth); this approach is faster during training and easier to understand than the other possible approach based on feeding prefixes of the captions to the model and predicting a single word for prefix example. The input caption is needed since we apply teacher forcing while training.
The following diagram shows the components of the model during training (in red the losses at each timestep).
The project is organized in such a way as to make it easy to modify the Keras model and provides scripts for training, evaluating, and using the model. It is possible to download the Flickr8k dataset using the Kaggle API by running a project script and transform the dataset using the Tensorflow Data API, which manages the dataset split based on the official train, test, and validation set divisions.
Install the Python requirements in your virtual environment.
The dataset used is Flickr8k, containing 8000 images, where each image is associated with five different captions.
From the project root you can run
python src/data/download_dataset.py
to download the dataset from Kaggle. You will need to setup your Kaggle username and API key locally (instructions). Check if the dataset has been correctly downloaded inside /data/raw.
You can then run
python src/data/make_dataset.py
to transform the captions into sequences using the Spacy custom tokenizer contained in this project and store the train/val/test sets inside /data/processed. A custom Spacy tokenizer has been preferred to the Tensorflow tokenizer, since Spacy has support for multiple languages and it's possible to adapt the behavior of this custom implementation to different use cases.
The splits will be stored as TFRecords containing examples made of the images together with their five captions. Using the TFRecords will make it easy to use Tensorflow's Data API to train and evaluate the model. Each TFRecord contains 200 examples. The ids of the images in the split are stored in the .txt files in /data/raw, already loaded in the repository. This should be the official split proposed by the creators of Flickr8k.
You can modify the model in the source at models/model.py and change the training parameters in models/train_model.py. Then training can be started with
python src/models/train_model.py
and it will show the loss and accuracy (considering teacher forcing) of the model at training time. It will also compute the same values on the validation dataset. The configuration and the weights of the trained model are saved inside /models/config and /models/weights (with the same filename - except for the extension). By saving the model confiuration, you can experiment with different training and model options.
The default training configuration is inspired to the one proposed in the paper, but different options can be provided via command-line when running the training script. You can check the training options to configure the model and the training process via
python src/models/train_model.py --help
To make predictions over custom images, insert your *.jpg files inside the /data/custom directory. Then run
python src/models/predict_model.py
to show the generated captions in the terminal. Use the option --model_filename to specify the filename (without extension) of the model you want to restore from the /models/config folder. If the option is unspecified, the last model (alphabetical and chronological order if the names are not changed) will be loaded.
Finally, evaluate the model with
python src/models/evaluate_model.py
to compute the BLEU-1 and BLEU-4 scores for the model on the Flickr8k test or val set (specify the --model_filename and --mode option).
Sample predictions on the Flickr8k validation set after 15 epochs with the default model configuration, with a beam width of 3:
Notice that the validation loss was still dropping after 15 epochs, so model training could have been continued to achieve better results. Nevertheless, the model seems to be learning some useful patterns for describing the images.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience
Contributions of any type (code, docs, suggestions...) are highly appreciated! Feel free to open an issue and to ask questions with the question label.
[1] Vinyals, Oriol, et al. "Show and tell: A neural image caption generator." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
[2] Tanti, Marc, Albert Gatt, and Kenneth P. Camilleri. "Where to put the image in an image caption generator." Natural Language Engineering 24.3 (2018): 467-489.