Metadata-Version: 2.1
Name: easy-vqa
Version: 1.0b1
Summary: The official package for the easy-VQA dataset.
Home-page: http://github.com/vzhou842/easy-VQA
Author: Victor Zhou
Author-email: vzhou842@gmail.com
License: UNKNOWN
Project-URL: Demo, https://easy-vqa-demo.victorzhou.com
Description: # easy-vqa
        
        [![Build Status](https://travis-ci.com/vzhou842/easy-vqa.svg?branch=master)](https://travis-ci.com/vzhou842/easy-vqa)
        ![PyPI](https://img.shields.io/pypi/v/easy-vqa)
        
        The official repository for the easy-VQA dataset. Contains:
        - the official Python package for the dataset
        - the source code for generating the dataset
        
        ## About the Dataset
        
        easy-VQA contains
        
        - 4000 train images and 38575 train questions.
        - 1000 test images and 9673 test questions.
        - 13 total possible answers.
        - 28407 training questions that are yes/no.
        - 7136 testing questions that are yes/no.
        
        All images are 64x64 color images. See a [live demo](https://easy-vqa-demo.victorzhou.com/) of a model trained on the dataset.
        
        ### Example Images
        
        ![](./easy_vqa/data/train/images/0.png)
        ![](./easy_vqa/data/train/images/1.png)
        ![](./easy_vqa/data/train/images/2.png)
        ![](./easy_vqa/data/train/images/3.png)
        ![](./easy_vqa/data/train/images/5.png)
        ![](./easy_vqa/data/train/images/6.png)
        ![](./easy_vqa/data/train/images/7.png)
        ![](./easy_vqa/data/train/images/8.png)
        
        ### Example Questions
        
        - _What color is the rectangle?_
        - _Does the image contain a triangle?_
        - _Is no blue shape present?_
        - _What shape does the image contain?_
        
        ## Installing the Package
        
        `pip install easy-vqa`
        
        ## Using the Package
        
        ### Questions
        
        Each question has 3 parts:
        - the question text
        - the answer
        - the image ID
        
        The question getters return corresponding arrays for each of the 3 parts:
        
        ```python
        from easy_vqa import get_train_questions, get_test_questions
        
        train_questions, train_answers, train_image_ids = get_train_questions()
        test_questions, test_answers, test_image_ids = get_test_questions()
        
        # Question 0 is at index 0 for all 3 arrays:
        print(train_questions[0]) # what shape does the image contain?
        print(train_answers[0])   # circle
        print(train_image_ids[0]) # 0
        ```
        
        ### Images
        
        The image path getters return dicts that map image ID to absolute paths that can be used to load the image.
        
        ```python
        from easy_vqa import get_train_image_paths, get_test_image_paths
        
        train_image_paths = get_train_image_paths()
        test_image_paths = get_test_image_paths()
        
        print(train_image_paths[0]) # ends in easy_vqa/data/train/images/0.png
        ```
        
        ### Answers
        
        The answers getter returns an array of all possible answers.
        
        ```python
        from easy_vqa import get_answers
        
        answers = get_answers()
        
        print(answers) # ['teal', 'brown', 'black', 'gray', 'yes', 'blue', 'rectangle', 'yellow', 'triangle', 'red', 'circle', 'no', 'green']
        ```
        
        ## Generating the Dataset
        
        The easy-VQA dataset was generated by running
        
        ```shell
        python gen_data/generate_data.py
        ```
        
        which writes to the `easy_vqa/data/` directory. Be sure to install the dependencies for dataset generation running `generate_data.py`:
        
        ```shell
        pip install -r gen_data/requirements.txt
        ```
        
        If you want to generate a larger easy-VQA dataset, simply modify the `NUM_TRAIN` and `NUM_TEST` constants in `generate_data.py`. Otherwise, if you want to modify the dataset itself, the files and code in the `gen_data/` directory should be pretty self-explanatory.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
