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---
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task_categories:
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- object-detection
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tags:
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- roboflow
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- roboflow2huggingface
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---
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<div align="center">
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<img width="640" alt="JijoJS/car-damage-new1" src="https://huggingface.co/datasets/JijoJS/car-damage-new1/resolve/main/thumbnail.jpg">
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</div>
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### Dataset Labels
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```
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['dent', 'scratch']
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```
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### Number of Images
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```json
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{'valid': 120, 'test': 120, 'train': 2187}
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```
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### How to Use
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- Install [datasets](https://pypi.org/project/datasets/):
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```bash
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pip install datasets
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```
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- Load the dataset:
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```python
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from datasets import load_dataset
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ds = load_dataset("JijoJS/car-damage-new1", name="full")
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example = ds['train'][0]
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```
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### Roboflow Dataset Page
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[https://universe.roboflow.com/jijo/car-damage-new/dataset/2](https://universe.roboflow.com/jijo/car-damage-new/dataset/2?ref=roboflow2huggingface)
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### Citation
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```
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@misc{
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car-damage-new_dataset,
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title = { car-damage-new Dataset },
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type = { Open Source Dataset },
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author = { JIJO },
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howpublished = { \\url{ https://universe.roboflow.com/jijo/car-damage-new } },
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url = { https://universe.roboflow.com/jijo/car-damage-new },
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journal = { Roboflow Universe },
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publisher = { Roboflow },
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year = { 2024 },
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month = { may },
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note = { visited on 2024-05-08 },
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}
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```
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### License
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CC BY 4.0
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### Dataset Summary
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This dataset was exported via roboflow.com on May 6, 2024 at 7:49 PM GMT
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Roboflow is an end-to-end computer vision platform that helps you
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* collaborate with your team on computer vision projects
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* collect & organize images
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* understand and search unstructured image data
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* annotate, and create datasets
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* export, train, and deploy computer vision models
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* use active learning to improve your dataset over time
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For state of the art Computer Vision training notebooks you can use with this dataset,
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visit https://github.com/roboflow/notebooks
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To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
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The dataset includes 2427 images.
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Scratch-dLwl are annotated in COCO format.
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The following pre-processing was applied to each image:
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* Auto-orientation of pixel data (with EXIF-orientation stripping)
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* Resize to 416x416 (Stretch)
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No image augmentation techniques were applied.
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