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README.md DELETED
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- ---
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- dataset_info:
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- features:
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- - name: image_id
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- dtype: int64
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- - name: image
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- dtype: image
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- - name: width
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- dtype: int32
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- - name: height
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- dtype: int32
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- - name: objects
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- sequence:
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- - name: id
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- dtype: int64
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- - name: area
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- dtype: int64
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- - name: bbox
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- sequence: float32
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- length: 4
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- - name: category
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- dtype:
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- class_label:
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- names:
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- '0': wall-damage
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- '1': Minorrotation
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- '2': Moderaterotation
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- '3': Severerotation
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- annotations_creators:
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- - crowdsourced
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- language_creators:
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- - found
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- language:
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- - en
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- license:
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- - cc
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- multilinguality:
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- - monolingual
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- size_categories:
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- - 1K<n<10K
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- source_datasets:
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- - original
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- task_categories:
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- - object-detection
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- task_ids: []
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- pretty_name: wall-damage
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- tags:
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- - rf100
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- ---
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-
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- # Dataset Card for wall-damage
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-
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- ** The original COCO dataset is stored at `dataset.tar.gz`**
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-
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- ## Dataset Description
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-
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- - **Homepage:** https://universe.roboflow.com/object-detection/wall-damage
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- - **Point of Contact:** [email protected]
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-
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- ### Dataset Summary
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-
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- wall-damage
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-
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- ### Supported Tasks and Leaderboards
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-
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- - `object-detection`: The dataset can be used to train a model for Object Detection.
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-
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- ### Languages
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-
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- English
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-
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- ## Dataset Structure
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-
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- ### Data Instances
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-
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- A data point comprises an image and its object annotations.
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-
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- ```
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- {
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- 'image_id': 15,
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- 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
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- 'width': 964043,
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- 'height': 640,
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- 'objects': {
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- 'id': [114, 115, 116, 117],
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- 'area': [3796, 1596, 152768, 81002],
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- 'bbox': [
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- [302.0, 109.0, 73.0, 52.0],
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- [810.0, 100.0, 57.0, 28.0],
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- [160.0, 31.0, 248.0, 616.0],
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- [741.0, 68.0, 202.0, 401.0]
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- ],
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- 'category': [4, 4, 0, 0]
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- }
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- }
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- ```
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-
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- ### Data Fields
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-
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- - `image`: the image id
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- - `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
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- - `width`: the image width
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- - `height`: the image height
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- - `objects`: a dictionary containing bounding box metadata for the objects present on the image
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- - `id`: the annotation id
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- - `area`: the area of the bounding box
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- - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
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- - `category`: the object's category.
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-
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-
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- #### Who are the annotators?
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-
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- Annotators are Roboflow users
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-
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- ## Additional Information
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-
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- ### Licensing Information
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-
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- See original homepage https://universe.roboflow.com/object-detection/wall-damage
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-
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- ### Citation Information
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-
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- ```
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- @misc{ wall-damage,
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- title = { wall damage Dataset },
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- type = { Open Source Dataset },
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- author = { Roboflow 100 },
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- howpublished = { \url{ https://universe.roboflow.com/object-detection/wall-damage } },
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- url = { https://universe.roboflow.com/object-detection/wall-damage },
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- journal = { Roboflow Universe },
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- publisher = { Roboflow },
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- year = { 2022 },
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- month = { nov },
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- note = { visited on 2023-03-29 },
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- }"
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- ```
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-
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- ### Contributions
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-
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- Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- {"description": "\nwall damage - v1 2022-08-04 6:29am\n==============================\n\nThis dataset was exported via roboflow.com on August 4, 2022 at 1:29 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 461 images.\nWall-damage are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 640x640 (Stretch)\n\nNo image augmentation techniques were applied.\n\n\n", "citation": "@misc{ wall-damage,\n title = { wall damage Dataset },\n type = { Open Source Dataset },\n author = { Roboflow 100 },\n howpublished = { \\url{ https://universe.roboflow.com/object-detection/wall-damage } },\n url = { https://universe.roboflow.com/object-detection/wall-damage },\n journal = { Roboflow Universe },\n publisher = { Roboflow },\n year = { 2022 },\n month = { nov },\n note = { visited on 2023-03-29 },\n}\"", "homepage": "https://universe.roboflow.com/object-detection/wall-damage", "license": "CC BY 4.0", "features": {"image_id": {"dtype": "int64", "_type": "Value"}, "image": {"_type": "Image"}, "width": {"dtype": "int32", "_type": "Value"}, "height": {"dtype": "int32", "_type": "Value"}, "objects": {"feature": {"id": {"dtype": "int64", "_type": "Value"}, "area": {"dtype": "int64", "_type": "Value"}, "bbox": {"feature": {"dtype": "float32", "_type": "Value"}, "length": 4, "_type": "Sequence"}, "category": {"names": ["wall-damage", "Minorrotation", "Moderaterotation", "Severerotation"], "_type": "ClassLabel"}}, "_type": "Sequence"}}, "builder_name": "dataset", "config_name": "default", "version": {"version_str": "1.0.0", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 9841054, "num_examples": 325, "dataset_name": "dataset"}, "validation": {"name": "validation", "num_bytes": 1307840, "num_examples": 40, "dataset_name": "dataset"}, "test": {"name": "test", "num_bytes": 2982909, "num_examples": 96, "dataset_name": "dataset"}}, "download_checksums": {"https://huggingface.co/datasets/Francesco/wall-damage/resolve/main/dataset.tar.gz": {"num_bytes": 13666719, "checksum": null}}, "download_size": 13666719, "dataset_size": 14131803, "size_in_bytes": 27798522}