Spaces:
Running
Running
| Python API and Evaluation Code for v2.0 and v1.0 releases of the VQA dataset. | |
| =================== | |
| ## VQA v2.0 release ## | |
| This release consists of | |
| - Real | |
| - 82,783 MS COCO training images, 40,504 MS COCO validation images and 81,434 MS COCO testing images (images are obtained from [MS COCO website] (http://mscoco.org/dataset/#download)) | |
| - 443,757 questions for training, 214,354 questions for validation and 447,793 questions for testing | |
| - 4,437,570 answers for training and 2,143,540 answers for validation (10 per question) | |
| There is only one type of task | |
| - Open-ended task | |
| ## VQA v1.0 release ## | |
| This release consists of | |
| - Real | |
| - 82,783 MS COCO training images, 40,504 MS COCO validation images and 81,434 MS COCO testing images (images are obtained from [MS COCO website] (http://mscoco.org/dataset/#download)) | |
| - 248,349 questions for training, 121,512 questions for validation and 244,302 questions for testing (3 per image) | |
| - 2,483,490 answers for training and 1,215,120 answers for validation (10 per question) | |
| - Abstract | |
| - 20,000 training images, 10,000 validation images and 20,000 MS COCO testing images | |
| - 60,000 questions for training, 30,000 questions for validation and 60,000 questions for testing (3 per image) | |
| - 600,000 answers for training and 300,000 answers for validation (10 per question) | |
| There are two types of tasks | |
| - Open-ended task | |
| - Multiple-choice task (18 choices per question) | |
| ## Requirements ## | |
| - python 2.7 | |
| - scikit-image (visit [this page](http://scikit-image.org/docs/dev/install.html) for installation) | |
| - matplotlib (visit [this page](http://matplotlib.org/users/installing.html) for installation) | |
| ## Files ## | |
| ./Questions | |
| - For v2.0, download the question files from the [VQA download page](http://www.visualqa.org/download.html), extract them and place in this folder. | |
| - For v1.0, both real and abstract, question files can be found on the [VQA v1 download page](http://www.visualqa.org/vqa_v1_download.html). | |
| - Question files from Beta v0.9 release (123,287 MSCOCO train and val images, 369,861 questions, 3,698,610 answers) can be found below | |
| - [training question files](http://visualqa.org/data/mscoco/prev_rel/Beta_v0.9/Questions_Train_mscoco.zip) | |
| - [validation question files](http://visualqa.org/data/mscoco/prev_rel/Beta_v0.9/Questions_Val_mscoco.zip) | |
| - Question files from Beta v0.1 release (10k MSCOCO images, 30k questions, 300k answers) can be found [here](http://visualqa.org/data/mscoco/prev_rel/Beta_v0.1/Questions_Train_mscoco.zip). | |
| ./Annotations | |
| - For v2.0, download the annotations files from the [VQA download page](http://www.visualqa.org/download.html), extract them and place in this folder. | |
| - For v1.0, for both real and abstract, annotation files can be found on the [VQA v1 download page](http://www.visualqa.org/vqa_v1_download.html). | |
| - Annotation files from Beta v0.9 release (123,287 MSCOCO train and val images, 369,861 questions, 3,698,610 answers) can be found below | |
| - [training annotation files](http://visualqa.org/data/mscoco/prev_rel/Beta_v0.9/Annotations_Train_mscoco.zip) | |
| - [validation annotation files](http://visualqa.org/data/mscoco/prev_rel/Beta_v0.9/Annotations_Val_mscoco.zip) | |
| - Annotation files from Beta v0.1 release (10k MSCOCO images, 30k questions, 300k answers) can be found [here](http://visualqa.org/data/mscoco/prev_rel/Beta_v0.1/Annotations_Train_mscoco.zip). | |
| ./Images | |
| - For real, create a directory with name mscoco inside this directory. For each of train, val and test, create directories with names train2014, val2014 and test2015 respectively inside mscoco directory, download respective images from [MS COCO website](http://mscoco.org/dataset/#download) and place them in respective folders. | |
| - For abstract, create a directory with name abstract_v002 inside this directory. For each of train, val and test, create directories with names train2015, val2015 and test2015 respectively inside abstract_v002 directory, download respective images from [VQA download page](http://www.visualqa.org/download.html) and place them in respective folders. | |
| ./PythonHelperTools | |
| - This directory contains the Python API to read and visualize the VQA dataset | |
| - vqaDemo.py (demo script) | |
| - vqaTools (API to read and visualize data) | |
| ./PythonEvaluationTools | |
| - This directory contains the Python evaluation code | |
| - vqaEvalDemo.py (evaluation demo script) | |
| - vqaEvaluation (evaluation code) | |
| ./Results | |
| - OpenEnded_mscoco_train2014_fake_results.json (an example of a fake results file for v1.0 to run the demo) | |
| - Visit [VQA evaluation page] (http://visualqa.org/evaluation) for more details. | |
| ./QuestionTypes | |
| - This directory contains the following lists of question types for both real and abstract questions (question types are unchanged from v1.0 to v2.0). In a list, if there are question types of length n+k and length n with the same first n words, then the question type of length n does not include questions that belong to the question type of length n+k. | |
| - mscoco_question_types.txt | |
| - abstract_v002_question_types.txt | |
| ## References ## | |
| - [VQA: Visual Question Answering](http://visualqa.org/) | |
| - [Microsoft COCO](http://mscoco.org/) | |
| ## Developers ## | |
| - Aishwarya Agrawal (Virginia Tech) | |
| - Code for API is based on [MSCOCO API code](https://github.com/pdollar/coco). | |
| - The format of the code for evaluation is based on [MSCOCO evaluation code](https://github.com/tylin/coco-caption). | |