| --- |
| license: cc-by-4.0 |
| task_categories: |
| - object-detection |
| tags: |
| - roboflow |
| - roboflow-100 |
| - rf100 |
| - yolo |
| - libreyolo |
| - documents |
| - computer-vision |
| - bounding-box |
| pretty_name: "Tweeter Posts" |
| size_categories: |
| - 1K<n<10K |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: string |
| splits: |
| - name: train |
| num_examples: 87 |
| - name: validation |
| num_examples: 21 |
| - name: test |
| num_examples: 9 |
| --- |
| |
| # Tweeter Posts |
|
|
| This dataset is part of the **Roboflow 100** benchmark, a diverse collection of 100 object detection datasets spanning 7 imagery domains. |
|
|
| ## Dataset Description |
|
|
| - **Source:** [Roboflow 100](https://github.com/roboflow/roboflow-100-benchmark) |
| - **Category:** Documents |
| - **License:** CC-BY-4.0 |
| - **Format:** YOLO (LibreYOLO compatible) |
| - **Mirrored on:** 2026-01-20 |
|
|
| ## Dataset Statistics |
|
|
| | Split | Images | |
| |-------|--------| |
| | Train | 87 | |
| | Validation | 21 | |
| | Test | 9 | |
| | **Total** | **117** | |
|
|
| ## Classes (2) |
|
|
| - caption |
| - tweet |
|
|
| ## Usage |
|
|
| ### With LibreYOLO |
|
|
| ```python |
| from libreyolo import LIBREYOLO |
| |
| # Load a model |
| model = LIBREYOLO(model_path="libreyoloXnano.pt") |
| |
| # Train on this dataset |
| model.train(data='path/to/data.yaml', epochs=100) |
| ``` |
|
|
| ### Download from HuggingFace |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| # Download the dataset |
| snapshot_download( |
| repo_id="Libre-YOLO/tweeter-posts", |
| repo_type="dataset", |
| local_dir="./tweeter-posts" |
| ) |
| ``` |
|
|
| ## Directory Structure |
|
|
| ``` |
| tweeter-posts/ |
| ├── data.yaml # Dataset configuration |
| ├── README.md # This file |
| ├── train/ |
| │ ├── images/ # Training images |
| │ └── labels/ # Training labels (YOLO format) |
| ├── valid/ |
| │ ├── images/ # Validation images |
| │ └── labels/ # Validation labels |
| └── test/ |
| ├── images/ # Test images (if available) |
| └── labels/ # Test labels |
| ``` |
|
|
| ## Label Format |
|
|
| Labels are in YOLO format (one `.txt` file per image): |
| ``` |
| <class_id> <x_center> <y_center> <width> <height> |
| ``` |
| All coordinates are normalized to [0, 1]. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the Roboflow 100 benchmark: |
|
|
| ```bibtex |
| @misc{rf100_2022, |
| Author = {Floriana Ciaglia and Francesco Saverio Zuppichini and Paul Guerrie and Mark McQuade and Jacob Solawetz}, |
| Title = {Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark}, |
| Year = {2022}, |
| Eprint = {arXiv:2211.13523}, |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under the **CC-BY-4.0** license. |
| Please check the original source for any additional terms. |
|
|
| ## Acknowledgments |
|
|
| - Original dataset from [Roboflow Universe](https://universe.roboflow.com/roboflow-100/tweeter-posts) |
| - Part of the [Roboflow 100 Benchmark](https://www.rf100.org/) |
| - Sponsored by Intel |
|
|
