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  1. wheel-defect-detection/README.dataset.txt +57 -0
  2. wheel-defect-detection/README.roboflow.txt +27 -0
  3. wheel-defect-detection/test/_annotations.coco.json +1 -0
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wheel-defect-detection/README.dataset.txt ADDED
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+ # wheel-defect-detection-e53jb-38chk-ytwg > 2025-03-13 8:06am
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+ https://universe.roboflow.com/rf-100-vl/wheel-defect-detection-e53jb-38chk-ytwg
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+
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+ Provided by a Roboflow user
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+ License: MIT
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+
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+ # Overview
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+ - [Introduction](#introduction)
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+ - [Object Classes](#object-classes)
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+ - [Cracks-Scratches](#cracks-scratches)
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+ - [Discoloration](#discoloration)
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+ - [Shelling](#shelling)
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+ - [Wheel](#wheel)
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+
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+ # Introduction
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+ This dataset is designed to detect and analyze defects in wheels. It includes four main classes to help identify potential issues that could affect performance and safety.
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+
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+ Classes:
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+ - **Cracks-Scratches**: Visible lines or marks on the wheel that indicate damage.
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+ - **Discoloration**: Areas where the wheel surface shows abnormal color changes.
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+ - **Shelling**: Parts of the wheel surface that appear pitted or hollowed out.
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+ - **Wheel**: The entire wheel structure.
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+
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+ # Object Classes
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+
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+ ## Cracks-Scratches
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+ ### Description
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+ Lines or seams that appear on the wheel’s surface, often consistent and sometimes deep or shallow.
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+
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+ ### Instructions
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+ - Annotate visible lines and marks that trace across the wheel’s surface.
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+ - The annotations should capture the full extent of each line.
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+ - Do not include smudges or dirt that do not have defined linear shapes.
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+
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+ ## Discoloration
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+ ### Description
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+ Patches or areas on the wheel where the color differs from the rest of the material, indicating potential corrosion or fading.
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+
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+ ### Instructions
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+ - Focus on areas where the color noticeably changes, usually darker or lighter than the surrounding region.
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+ - Exclude regions where lighting changes cause temporary shadows or highlights.
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+
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+ ## Shelling
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+ ### Description
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+ Areas on the wheel surface that are hollowed or indented, resembling small craters or pits.
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+
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+ ### Instructions
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+ - Outline regions that appear to be missing material, forming depressions on the wheel.
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+ - Avoid marking areas that are merely dirty or have surface residue but are not structurally hollowed.
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+
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+ ## Wheel
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+ ### Description
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+ The entire circular structure, including spokes and hubs, typically made of metal or alloy.
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+
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+ ### Instructions
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+ - Locate and enclose the entire wheel, ensuring that the edges of the wheel are precisely defined.
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+ - Include all visible parts of the wheel, but do not extend the annotation over occluding objects such as nearby parts or machinery.
wheel-defect-detection/README.roboflow.txt ADDED
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+
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+ wheel-defect-detection-e53jb-38chk-ytwg - v1 2025-03-13 8:06am
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+ ==============================
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+
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+ This dataset was exported via roboflow.com on March 14, 2025 at 6:27 AM GMT
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+
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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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+
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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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+
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+ To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
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+
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+ The dataset includes 309 images.
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+ Wheel-defect-detection-e53jb-38chk-ytwg are annotated in COCO format.
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+
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+ The following pre-processing was applied to each image:
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+
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+ No image augmentation techniques were applied.
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+
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+
wheel-defect-detection/test/_annotations.coco.json ADDED
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+ {"info": {"year": "2025", "version": "1", "description": "Exported from roboflow.com", "contributor": "", "url": "https://public.roboflow.com/object-detection/undefined", "date_created": "2025-03-14T06:27:12+00:00"}, "licenses": [{"id": 1, "url": "https://choosealicense.com/licenses/mit/", "name": "MIT"}], "categories": [{"id": 0, "name": "Cracks-Scratches", "supercategory": "wheel-defect-detection-e53jb-38chk-ytwg"}, {"id": 1, "name": "Discoloration", "supercategory": "wheel-defect-detection-e53jb-38chk-ytwg"}, {"id": 2, "name": "Shelling", "supercategory": "wheel-defect-detection-e53jb-38chk-ytwg"}, {"id": 3, "name": "Wheel", "supercategory": "wheel-defect-detection-e53jb-38chk-ytwg"}], "images": [{"id": 0, "license": 1, "file_name": "frame5982_jpg.rf.1023929bbd73ccabe914a36df1b61de9.jpg", "height": 640, "width": 640, "date_captured": "2025-03-14T06:27:12+00:00", "extra": {"name": "frame5982_jpg"}}, {"id": 1, "license": 1, "file_name": 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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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Git LFS Details

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