Datasets:

HichTala
/

dior

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DIOR Hugging Face-Ready Vision Dataset

This dataset is a restructured version of the DIOR (Object Detection in Optical Remote Sensing Images), specifically designed to simplify object detection workflows. By converting them to the COCO format, this project provides an easier way to use DIOR with popular computer vision frameworks. Additionally, the dataset is formatted for seamless integration with Hugging Face datasets, unlocking new possibilities for training and experimentation.

📂 Dataset Structure

COCO Format

The dataset follows the COCO dataset structure, making it straightforward to work with:

dior/  
├── annotations/  
│   ├── instances_train.json  
│   ├── instances_val.json  
│   └── instances_test.json  
├── train/
├── val/
├── test/

Hugging Face Format

The dataset is compatible with the datasets library. You can load it directly using:

from datasets import load_dataset  

dataset = load_dataset("HichTala/dior")

🖼️ Sample Visualizations

Above: An example of resized images with bounding boxes in COCO format.

🚀 Getting Started

Install Required Libraries

Install datasets for Hugging Face compatibility:

pip install datasets

Use any object detection framework supporting COCO format for training.

Load the Dataset

Hugging Face:

from datasets import load_dataset  

dataset = load_dataset("HichTala/dior")  
train_data = dataset["train"]

Custom Script for COCO-Compatible Frameworks:

import json  
from pycocotools.coco import COCO

coco = COCO("annotations/train.json")

see demo notebook here for more details.

📚 Used in Research

This processed version of DIOR has been used in the paper:
📄 LoRA for Cross-Domain Few-Shot Object Detection
The dataset served as a target domain for evaluating the generalization capabilities of diffusion-based object detectors in low-data regimes.

📝 How to Cite

If you use this dataset, please consider citing the original DIOR dataset:

@article{Li_2020,
   title={Object detection in optical remote sensing images: A survey and a new benchmark},
   volume={159},
   ISSN={0924-2716},
   url={http://dx.doi.org/10.1016/j.isprsjprs.2019.11.023},
   DOI={10.1016/j.isprsjprs.2019.11.023},
   journal={ISPRS Journal of Photogrammetry and Remote Sensing},
   publisher={Elsevier BV},
   author={Li, Ke and Wan, Gang and Cheng, Gong and Meng, Liqiu and Han, Junwei},
   year={2020},
   month=jan, pages={296–307}}

Additionally, you can mention this repository for the resized COCO and Hugging Face formats.

Enjoy using DIOR in coco format for your object detection experiments! 🚀