Datasets:
Tasks:
Object Detection
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
Update README.md
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README.md
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DOTA 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.
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## 🌟 Key Features
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Resized Images: Reduced image dimensions for faster training and inference while maintaining key details.
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COCO Format: Compatible with major deep learning libraries like PyTorch, TensorFlow, and MMDetection.
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Hugging Face Integration: Ready-to-use with the Hugging Face datasets library for efficient data loading and preprocessing.
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## 📂 Dataset Structure
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### COCO Format
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The dataset follows the COCO dataset structure, making it straightforward to work with:
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DOTA 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.
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## 📂 Dataset Structure
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### COCO Format
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The dataset follows the COCO dataset structure, making it straightforward to work with:
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