Update README.md

README.md

@@ -60,3 +60,93 @@ configs:
   - split: test
     path: data/test-*
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+
+# DOTA-COCO: Resized and Hugging Face-Ready Vision Dataset
+
+DOTA-COCO is a restructured version of the DOTA (Dataset for Object Detection in Aerial Images), specifically designed to simplify object detection workflows. By resizing the original images and converting them to the COCO format, this project provides an easier way to use DOTA with popular computer vision frameworks. Additionally, the dataset is formatted for seamless integration with Hugging Face datasets, unlocking new possibilities for training and experimentation.
+
+## 🌟 Key Features
+Resized Images: Reduced image dimensions for faster training and inference while maintaining key details.
+COCO Format: Compatible with major deep learning libraries like PyTorch, TensorFlow, and MMDetection.
+Hugging Face Integration: Ready-to-use with the Hugging Face datasets library for efficient data loading and preprocessing.
+
+## 📂 Dataset Structure
+### COCO Format
+The dataset follows the COCO dataset structure, making it straightforward to work with:
+
+```plaintext
+dota/  
+├── 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/dota")
+```
+
+## 🖼️ 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/dota")  
+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.
+
+## 📝 How to Cite
+If you use this dataset, please consider citing the original DOTA dataset:
+
+```plaintext
+Copy code
+@inproceedings{Xia_2018_CVPR,  
+    author = {Gui-Song Xia and Xiang Bai and Jieqing Zheng and others},  
+    title = {DOTA: A Large-Scale Dataset for Object Detection in Aerial Images},  
+    booktitle = {CVPR},  
+    year = {2018}  
+}  
+```
+
+Additionally, you can mention this repository for the resized COCO and Hugging Face formats.
+
+
+Enjoy using DOTA in coco format for your object detection experiments! 🚀
+
+