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README.md

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-license: mit
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+# Temporal Logic Video (TLV) Dataset
+## Overview
+<!-- PROJECT LOGO -->
+<br />
+<div align="center">
+  <a href="https://github.com/UTAustin-SwarmLab/temporal-logic-video-dataset">
+    <img src="images/logo.png" alt="Logo" width="240" height="240">
+  </a>
+
+  <h3 align="center">Temporal Logic Video (TLV) Dataset</h3>
+
+  <p align="center">
+    Synthetic and real video dataset with temporal logic annotation
+    <br />
+    <a href="https://github.com/UTAustin-SwarmLab/temporal-logic-video-dataset"><strong>Explore the docs »</strong></a>
+    <br />
+    <br />
+    <a href="https://anoymousu1.github.io/nsvs-anonymous.github.io/">NSVS-TL Project Webpage</a>
+    ·
+    <a href="https://github.com/UTAustin-SwarmLab/Neuro-Symbolic-Video-Search-Temploral-Logic">NSVS-TL Source Code</a>
+  </p>
+</div>
+
+## Overview
+
+The Temporal Logic Video (TLV) Dataset addresses the scarcity of state-of-the-art video datasets for long-horizon, temporally extended activity and object detection. It comprises two main components:
+
+1. Synthetic datasets: Generated by concatenating static images from established computer vision datasets (COCO and ImageNet), allowing for the introduction of a wide range of Temporal Logic (TL) specifications.
+2. Real-world datasets: Based on open-source autonomous vehicle (AV) driving datasets, specifically NuScenes and Waymo.
+
+## Table of Contents
+
+- [Dataset Composition](#dataset-composition)
+- [Dataset (Release)](#dataset)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Data Generation](#data-generation)
+- [Contribution Guidelines](#contribution-guidelines)
+- [License](#license)
+- [Acknowledgments](#acknowledgments)
+
+## Dataset Composition
+
+### Synthetic Datasets
+- Source: COCO and ImageNet
+- Purpose: Introduce artificial Temporal Logic specifications
+- Generation Method: Image stitching from static datasets
+
+### Real-world Datasets
+- Sources: NuScenes and Waymo
+- Purpose: Provide real-world autonomous vehicle scenarios
+- Annotation: Temporal Logic specifications added to existing data
+
+## Dataset 
+<div align="center">
+  <a href="https://github.com/UTAustin-SwarmLab/temporal-logic-video-dataset">
+    <img src="images/teaser.png" alt="Logo" width="840" height="440">
+  </a>
+</div>
+
+Though we provide a source code to generate datasets from different types of data sources, we release a dataset v1 as a proof of concept. 
+
+### Dataset Structure
+
+We provide a v1 dataset as a proof of concept. The data is offered as serialized objects, each containing a set of frames with annotations.
+
+#### File Naming Convention
+`\<tlv_data_type\>:source:\<datasource\>-number_of_frames:\<number_of_frames\>-\<uuid\>.pkl`
+
+#### Object Attributes
+Each serialized object contains the following attributes:
+- `ground_truth`: Boolean indicating whether the dataset contains ground truth labels
+- `ltl_formula`: Temporal logic formula applied to the dataset
+- `proposition`: A set of proposition for ltl_formula
+- `number_of_frame`: Total number of frames in the dataset
+- `frames_of_interest`: Frames of interest which satisfy the ltl_formula
+- `labels_of_frames`: Labels for each frame
+- `images_of_frames`: Image data for each frame
+
+You can download a dataset from here. The structure of dataset is as follows: serializer
+   ```
+   tlv-dataset-v1/
+   ├── tlv_real_dataset/
+   ├──── prop1Uprop2/
+   ├──── (prop1&prop2)Uprop3/
+   ├── tlv_synthetic_dataset/
+   ├──── Fprop1/
+   ├──── Gprop1/
+   ├──── prop1&prop2/
+   ├──── prop1Uprop2/
+   └──── (prop1&prop2)Uprop3/
+   ```
+#### Dataset Statistics
+1. Total Number of Frames
+
+| Ground Truth TL Specifications | Synthetic TLV Dataset | | Real TLV Dataset | |
+| --- | ---: | ---: | ---: | ---: |
+| | COCO | ImageNet | Waymo | Nuscenes |
+| Eventually Event A | - | 15,750 | - | - |
+| Always Event A | - | 15,750 | - | - |
+| Event A And Event B | 31,500 | - | - | - |
+| Event A Until Event B | 15,750 | 15,750 | 8,736 | 19,808 |
+| (Event A And Event B) Until Event C | 5,789 | - | 7,459 | 7,459 |
+
+2. Total Number of datasets
+
+| Ground Truth TL Specifications | Synthetic TLV Dataset | | Real TLV Dataset | |
+| --- | ---: | ---: | ---: | ---: |
+| | COCO | ImageNet | Waymo | Nuscenes |
+| Eventually Event A | - | 60 | - | - |
+| Always Event A | - | 60 | - | - |
+| Event A And Event B | 120 | - | - | - |
+| Event A Until Event B | 60| 60 | 45| 494 |
+| (Event A And Event B) Until Event C | 97 | - | 30 | 186|
+
+
+## Installation
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip build
+python -m pip install --editable ."[dev, test]"
+```
+
+### Prerequisites
+
+1. ImageNet (ILSVRC 2017):
+   ```
+   ILSVRC/
+   ├── Annotations/
+   ├── Data/
+   ├── ImageSets/
+   └── LOC_synset_mapping.txt
+   ```
+
+2. COCO (2017):
+   ```
+   COCO/
+   └── 2017/
+       ├── annotations/
+       ├── train2017/
+       └── val2017/
+   ```
+
+## Usage
+
+Detailed usage instructions for data loading and processing.
+
+### Data Loader Configuration
+
+- `data_root_dir`: Root directory of the dataset
+- `mapping_to`: Label mapping scheme (default: "coco")
+- `save_dir`: Output directory for processed data
+
+### Synthetic Data Generator Configuration
+
+- `initial_number_of_frame`: Starting frame count per video
+- `max_number_frame`: Maximum frame count per video
+- `number_video_per_set_of_frame`: Videos to generate per frame set
+- `increase_rate`: Frame count increment rate
+- `ltl_logic`: Temporal Logic specification (e.g., "F prop1", "G prop1")
+- `save_images`: Boolean flag for saving individual frames
+
+## Data Generation
+
+### COCO Synthetic Data Generation
+
+```bash
+python3 run_scripts/run_synthetic_tlv_coco.py --data_root_dir "../COCO/2017" --save_dir "<output_dir>"
+```
+
+### ImageNet Synthetic Data Generation
+
+```bash
+python3 run_synthetic_tlv_imagenet.py --data_root_dir "../ILSVRC" --save_dir "<output_dir>"
+```
+
+Note: ImageNet generator does not support '&' LTL logic formulae.
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+
+## Citation
+If you find this repo useful, please cite our paper:
+```bibtex
+@inproceedings{Choi_2024_ECCV,
+  author={Choi, Minkyu and Goel Harsh and Omama, Mohammad and Yang, Yunhao and Shah, Sahil and Chinchali and Sandeep},
+  title={Towards Neuro-Symbolic Video Understanding},
+  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
+  month={September},
+  year={2024}
+}
+```
+
+
+[contributors-shield]: https://img.shields.io/github/contributors/UTAustin-SwarmLab/temporal-logic-video-dataset.svg?style=for-the-badge
+[contributors-url]: https://github.com/UTAustin-SwarmLab/temporal-logic-video-dataset/graphs/contributors
+[forks-shield]: https://img.shields.io/github/forks/UTAustin-SwarmLab/temporal-logic-video-dataset.svg?style=for-the-badge
+[forks-url]: https://github.com/UTAustin-SwarmLab/temporal-logic-video-dataset/network/members
+[stars-shield]: https://img.shields.io/github/stars/UTAustin-SwarmLab/temporal-logic-video-dataset.svg?style=for-the-badge
+[stars-url]: https://github.com/UTAustin-SwarmLab/temporal-logic-video-dataset/stargazers
+[license-shield]: https://img.shields.io/github/license/UTAustin-SwarmLab/temporal-logic-video-dataset.svg?style=for-the-badge
+[license-url]: https://github.com/UTAustin-SwarmLab/temporal-logic-video-dataset/blob/master/LICENSE.txt