Update README.md

README.md

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       dtype: int64
   splits:
   - name: train
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+    num_bytes: 5902685454
     num_examples: 18000
   - name: test
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+    num_bytes: 1150035824
     num_examples: 3463
   - name: validation
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     num_examples: 2000
   download_size: 7626168863
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 configs:
 - config_name: default
   data_files:
@@ -63,4 +63,101 @@ configs:
     path: data/test-*
   - split: validation
     path: data/validation-*
+task_categories:
+- object-detection
+language:
+- en
+pretty_name: DIOR
 ---
+# 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:
+
+```plaintext
+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:
+
+```python
+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:
+
+```bash
+pip install datasets  
+```
+- Use any object detection framework supporting COCO format for training.
+
+### Load the Dataset
+#### Hugging Face:
+
+```python
+from datasets import load_dataset  
+
+dataset = load_dataset("HichTala/dior")  
+train_data = dataset["train"]  
+```
+
+#### Custom Script for COCO-Compatible Frameworks:
+```python
+import json  
+from pycocotools.coco import COCO
+
+coco = COCO("annotations/train.json")
+```
+
+see demo notebook [here](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoDemo.ipynb) for more details.
+
+## ⚙️ Preprocessing Details
+- Resizing: The original large images were split into smaller patches, each resized to *512x512 pixels*. 
+- Annotations: Converted to COCO format for better compatibility and flexibility.
+
+## 📚 Used in Research
+
+This processed version of DIOR has been used in the paper:
+📄 [LoRA for Cross-Domain Few-Shot Object Detection](https://huggingface.co/papers/2504.06330)
+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:
+
+```plaintext
+@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! 🚀