---
dataset_name: "Forbin Dataset"
tags:
  - humanities
  - digital-humanities
  - archives
  - historical-documents
  - text-detection
  - polygon-annotation
  - verso-recto photographs
license: cc-by-nc-4.0
task_categories:
  - object-detection
  - feature-extraction
  - image-classification
pretty_name: "Forbin Dataset: A collection of historical photographs with archival metadata"
---

# Forbin Dataset:  *A collection of historical photographs with archival metadata*

This repository hosts the *Forbin Dataset*, a large-scale collection of historical photographs taken or collected by **Victor Forbin (1868–1947)**.  

This HuggingFace dataset version provides:
- COCO-style annotations (segmentation polygons)  
- Archival metadata (Box ID, description, notes, dates when available)  
- A lightweight **explorer interface** (HTML/JS) to preview images and annotations: [https://mchelali.github.io/forbin_dataset/](https://mchelali.github.io/forbin_dataset/)

## 📜 Dataset Description

The Forbin Dataset contains digitized historical photographs from the personal archives of Victor Forbin, a French explorer, photographer, and writer.  
Images are accompanied by rich metadata and manually extracted segmentation polygons suitable for:

- Computer Vision  
- Document Analysis  
- Cultural Heritage Studies  
- Machine Learning Research  

The sample included here is intended for **illustration and early experimentation only**.  
The upcoming full release will contain tens of thousands of images with complete metadata and annotations.

## 🛠️ Data Access and Usage Instructions

Given the size of the image archives, the dataset must be loaded in a two-step process: **Local Download** followed by **Indexing**.

### 1\. Downloading the Raw Data Files (Images and Annotations) ⬇️

The dataset is distributed as WebDataset archives (`.tar`) and separate JSON annotation files. **You must download these files locally before starting the training process.**

| File | Content | Note |
| :--- | :--- | :--- |
| **`forbin_all.json`** | All Image IDs, metadata, and annotations (for annotated images). | Used for full dataset indexing. |
| **`forbin_annotated.json`** | Only images that have associated annotations (simplified index). | Useful for training on annotation tasks. |
| **`data/*.tar`** | WebDataset archives containing all raw images. | **Large files.** |

#### **Mode A: Via the Hugging Face Command Line Interface (CLI)**

This is the fastest method for users familiar with the terminal.

```bash
# Requires installation: pip install huggingface_hub
hf download  mchelali/forbin_dataset  --repo-type dataset --local-dir ./forbin_data_local 
```

#### **Mode B: Via Python (Recommended for Resumable Downloads)**

This reliable method uses the official Python API, which automatically handles resuming the download process if interrupted.

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="mchelali/forbin_dataset",
    repo_type="dataset",
    local_dir="./forbin_data_local" # Your chosen destination folder
)
```

#### **Web Download Interface (For SHS Researchers):**

For users less familiar with the command line, we provide a dedicated web interface to download the individual `.tar` archives one by one:

➡️ **Web Download Interface:** [https://mchelali.github.io/forbin\_dataset/download.html](https://mchelali.github.io/forbin_dataset/download.html)

-----

### 2\. Indexing and Annotation Usage 📚

Once the `*.json` and `*.tar` files are downloaded locally, you can build your own data loading pipeline.

**Annotation Format:**

All annotations (including textual metadata, bounding boxes, and segmentation polygons) are provided in the standard **COCO (Common Objects in Context) format**. This ensures compatibility with existing computer vision tools and libraries like PyTorch, TensorFlow, and `pycocotools`.

The JSON file acts as your **Manifest** (Index Table). It links the image ID (via `image_id`) to the image's location within the `.tar` archives (via the `file_names` field in the `images` section).

**To use the dataset:**

1.  Load the JSON file (`forbin_all.json` or `forbin_annotated.json`) into your program.
2.  Use the Python `tarfile` (or `webdataset`) library to open the corresponding `.tar` archive and load the image bytes based on the path provided in the `file_names` field.
3.  Apply the COCO annotations (found in the `annotations` section of the JSON) to the loaded image.

## 🔖 License

This sample dataset is released under the following license:

**Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)**  
➡️ https://creativecommons.org/licenses/by-nc/4.0/

This means:
- ✔ You must provide attribution  
- ✔ You may share and adapt the material  
- ❌ You may **not** use it for commercial purposes  


## 📚 Citation

If you use this dataset or the sample in academic work, please cite the forthcoming data paper:

```

[Under review] 
Chelali M., Gosselet S. K., Cloppet F., Kurtz C., Bloch I. and Foliard D.,
The Forbin Dataset: A collection of historical photographs with archival metadata, 2025.

```





## 🤝 Acknowledgment of Authors

This dataset originates from the personal archives of **Victor Forbin**, digitized and curated by the *High Vision Project – Archives & Vision Initiative*.  
All annotation and data processing work was performed by the project contributors.

This work is supported by the French National Research Agency under the **ANR-24-CE38-4079** project