Skip to content

Latest commit

 

History

History
100 lines (62 loc) · 3.69 KB

README.md

File metadata and controls

100 lines (62 loc) · 3.69 KB
Raw

Neural Image Caption Generation with Visual Attention

This repository contains a PyTorch implementation of the PMLR 2015 Paper, Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. For the original implementation of the paper, please refer to the author's implementation in Theano language here.

For further details on the Data Pre-Processing and Methodology, please refer to Report.pdf

Installing Dependencies

Make sure you have a Python3+ version. Run the following command -

pip install -r requirements.txt

Training the Model

Dataset

Download the dataset from here and unzip the downloaded dataset. This will create a ./data/ in the root folder of the repository.

Training

python3 train.py [--base_dir (str)] [--debug (bool)] [--lr (str)] [--alpha_c (float)] [--log_interval (int)] [--epochs (int)] [--batch_size (int)] [--result_dir (str)] [--init_model (str)]

Options :

--base_dir              Path of directory of data/ folder

--debug                 Debug : If set to True, then will print debug messages and will run only for one epoch with batch size one. 

--lr                    Learning Rate of Adam optimiser. 

--alpha_c               Regularisation Constant.

--log_interval          Number of batches after you want to print the loss for one epoch.

--epochs                No of epochs you want to train model. 

--batch_size            Batch Size to be used. 

--result_dir            Path of the directory where you want to create results/ folder which will save the trained models. 

--init_model            Path of the model with which you want to initialise the model before training.

Example Usage :

python3 train.py --base_dir=/kaggle/input/ass4q1/Data/ --result_dir=/kaggle/working/results --init_model=/kaggle/input/show-33/33.pth

Results

After training the model for 42 epochs, we got the following scores :

Data BLEU1 BLEU2 BLEU3 BLEU4 METEOR
Validation Data 0.5560103298543799 0.3012887274731861 0.16869948803000315 0.09124722446388864 0.24042306161781762
Testing Data 0.5756577733000581 0.3240567011948614 0.18865787383495358 0.10922486278026644 0.25408193751079544
Soft Attention for the generated images

Inference

Download the trained model from here

To finally test your trained model run the following command

python3 inferences.py [--base_dir (str)] [--model (str)] [--result_dir (int)]

Options :

--model                 Path of trained model

--base_dir              Path of directory of data/ folder

--result_dir            Path of the directory where you want to save the generated captions

License

Copyright (c) 2021 Paras Mehan

For license information, see LICENSE or http://mit-license.org

Done by Pragyan Mehrotra, Vrinda Narayan and Paras Mehan

This code was written as a part of a course group assignment in Deep Learning with Dr. Md. Shad Akhtar at IIIT Delhi during Winter 2021 Semester.

For bugs in the code, please write to: paras18062 [at] iiitd [dot] ac [dot] in or create an issue in the repository