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README.md

@@ -1,41 +1,71 @@
----
-license: apache-2.0
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: bboxes
-    sequence:
-      sequence: float32
-  - name: labels
-    sequence:
-      class_label:
-        names:
-          '0': Broken Cable
-          '1': Broken Insulator
-          '2': Cable
-          '3': Insulators
-          '4': Tower
-          '5': Vegetation
-  splits:
-  - name: train
-    num_bytes: 114060938.86
-    num_examples: 1794
-  - name: validation
-    num_bytes: 4561112.0
-    num_examples: 77
-  - name: test
-    num_bytes: 2287163.0
-    num_examples: 41
-  download_size: 122200157
-  dataset_size: 120909213.86
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
----
+
+# Powerline Components and Faults Dataset
+
+## Overview
+
+The **Powerline Components and Faults Dataset** is a dataset designed for object detection tasks involving powerline components and associated faults. It provides images of powerline infrastructure along with annotated bounding boxes for various components and faults.
+
+This dataset is useful for training and evaluating models on powerline inspection, maintenance, and safety applications.
+
+## Dataset Structure
+
+The dataset is organized into the following directories:
+
+- `train/`: Contains training images and their corresponding annotation files.
+- `validation/`: Contains validation images and their corresponding annotation files.
+- `test/`: Contains test images and their corresponding annotation files.
+
+Each image file has a corresponding `.txt` file in the same directory, which contains the annotations in YOLO format.
+
+## Data Format
+
+### Images
+
+- Format: JPEG/PNG
+- Resolution: Various resolutions
+
+### Annotations
+
+Annotations are provided in YOLO format, where each line in a `.txt` file corresponds to an object in the image. The format is:
+
+```
+class_id x_center y_center width height
+```
+
+- `class_id`: The ID of the object class.
+- `x_center`, `y_center`: The center of the bounding box (normalized between 0 and 1).
+- `width`, `height`: The dimensions of the bounding box (normalized between 0 and 1).
+
+## Usage
+
+You can use this dataset with popular machine learning frameworks and libraries. Below is an example of how to load the dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("docmhvr/powerline-components-and-faults")
+
+# Access the train, validation, and test splits
+train_dataset = dataset['train']
+val_dataset = dataset['validation']
+test_dataset = dataset['test']
+```
+
+## License
+
+This dataset is provided under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for more details.
+
+## Acknowledgements
+
+This dataset was created as part of the research work on powerline inspection and fault detection. Data was collected using DJI Mini drone and manually compiled and annotated using Roboflow.
+
+## Research reference
+
+You can find the related Research work published in IEEE, full text avaliable on researchgate here, 
+
+[Research Paper](https://www.researchgate.net/publication/381461493_UAV-Based_Powerline_Problem_Inspection_and_Classification_using_Machine_Learning_Approaches)
+
+## Contribution
+
+If you would like to contribute to this dataset, please feel free to open an issue or submit a pull request on the [GitHub repository](https://github.com/docmhvr/UAV-Based-Powerline-Problem-Inspection-Using-Machine-Learning).