| # LingoQA Datasets README |
|
|
| ## Overview |
|
|
| The LingoQA datasets comprise a collection of complementary datasets designed for training and evaluating machine learning models on video understanding and question-answering tasks. These datasets are categorized into three main types: action, scenery, and evaluation, each containing video segments, questions, and answers, along with associated images to aid in visual understanding tasks. |
|
|
| ## Directory Structure |
| The datasets are organized as follows: |
|
|
| ``` |
| LingoQA/ |
| ├── action/ |
| │ ├── train.parquet # Training data for action-related questions |
| │ └── images.zip # Zipped directory of images related to action segments |
| ├── scenery/ |
| │ ├── train.parquet # Training data for scenery-related questions |
| │ └── images.zip # Zipped directory of images related to scenery segments |
| └── evaluation/ |
| ├── val.parquet # Validation data for evaluating models |
| └── images.zip # Zipped directory of images related to evaluation segments |
| ``` |
|
|
| ## Data Format |
| Each .parquet file within the datasets contains the following columns: |
|
|
| - `segment_id`: An md5 hash that uniquely identifies each video segment. |
| - `question_id`: A unique identifier for each question associated with a video segment. |
| - `images`: An array of relative paths pointing to images that correspond to the video segment. Example format: ['images/train/hash_segment_1/0.jpg', ...]. |
| - `question`: The question text related to the video segment. |
| - `answer`: The answer text related to the question posed. |
|
|
|
|
| ## How to Use |
|
|
| ### Preparing the Data |
|
|
| 1. **Extract Images**: First, unzip the images.zip files in their respective directories to access the images associated with each dataset. |
|
|
| ```bash |
| unzip action/images.zip -d action/ |
| unzip scenery/images.zip -d scenery/ |
| unzip evaluation/images.zip -d evaluation/ |
| ``` |
|
|
| 2. **Load Datasets**: You can load the `.parquet` files using Python with libraries such as pandas or pyarrow. Here's an example of how to load a dataset: |
|
|
| ```python |
| import pandas as pd |
| |
| # Load an example dataset |
| action_train = pd.read_parquet('action/train.parquet') |
| ``` |
|
|
| ### Using the Data |
|
|
| - Model Training: Use the train.parquet files from the action and scenery datasets to train your machine learning models. These datasets provide a rich set of questions and answers along with images to facilitate training models capable of understanding and responding to queries about video content. |
|
|
| - Model Evaluation: The evaluation/val.parquet file is intended for validating the performance of your models. It offers a separate set of questions and answers to test your model's ability to generalize to new data. |
|
|
| ## License |
| Please ensure to review the license agreement associated with the LingoQA datasets before using them for your projects. |
|
|
