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b9479ca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # 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.
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