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README.md

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 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: image_id
-    dtype: string
-  - name: annotations
-    sequence:
-    - name: class_id
-      dtype: int32
-    - name: polygon
-      sequence: float32
-  - name: image_width
-    dtype: int32
-  - name: image_height
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 7353848.0
-    num_examples: 148
-  - name: valid
-    num_bytes: 1698831.0
-    num_examples: 33
-  - name: test
-    num_bytes: 1024678.0
-    num_examples: 20
-  download_size: 10090627
-  dataset_size: 10077357.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: valid
-    path: data/valid-*
-  - split: test
-    path: data/test-*
+license: cc-by-4.0
+task_categories:
+- object-detection
+tags:
+- chess
+- computer-vision
+- yolo
+- object-detection
+size_categories:
+- 1K<n<10K
 ---
+
+# Chess Piece Detection Dataset: chess-board-4
+
+## Dataset Description
+
+This dataset contains chess piece detection annotations in YOLOv8 format.
+
+Chess board segmentation dataset with polygon annotations for precise board detection and localization. Optimized for YOLOv8 segmentation training with chess-board class.
+
+## Dataset Structure
+
+The dataset follows the YOLOv8 format with the following structure:
+- `train/`: Training images and labels
+- `valid/`: Validation images and labels
+- `test/`: Test images and labels
+
+## Classes
+
+The dataset contains 12 classes of chess pieces:
+
+0. black-bishop
+1. black-king
+2. black-knight
+3. black-pawn
+4. black-queen
+5. black-rook
+6. white-bishop
+7. white-king
+8. white-knight
+9. white-pawn
+10. white-queen
+11. white-rook
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("dopaul/chess-board-4")
+
+# Access different splits
+train_data = dataset["train"]
+valid_data = dataset["valid"]
+test_data = dataset["test"]
+
+# Example: Access first training image and annotations
+example = train_data[0]
+image = example["image"]
+annotations = example["annotations"]
+```
+
+## Citation
+
+If you use this dataset, please consider citing the original sources and this repository.
+
+## License
+
+This dataset is released under the CC BY 4.0 license.

README.roboflow.txt

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+
+chess-board - v4 2025-06-26 12:51am
+==============================
+
+This dataset was exported via roboflow.com on June 26, 2025 at 7:55 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 201 images.
+Chess-board are annotated in YOLOv8 format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 640x640 (Stretch)
+
+No image augmentation techniques were applied.
+
+

data.yaml

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+names:
+- chess-board
+nc: 1
+roboflow:
+  license: CC BY 4.0
+  project: chess-board-i0ptl
+  url: https://app.roboflow.com/gustoguardian/chess-board-i0ptl/4
+  version: 4
+  workspace: gustoguardian
+test: test/images
+train: train/images
+val: valid/images