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@@ -24,19 +24,115 @@ dataset_info:
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  names:
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  '0': acl-x-ray
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  '1': acl
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- splits:
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- - name: train
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- num_bytes: 45899910.976
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- num_examples: 2141
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- - name: validation
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- num_bytes: 6576557.0
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- num_examples: 306
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- - name: test
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- num_bytes: 13306085.0
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- num_examples: 612
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- download_size: 65723141
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- dataset_size: 65782552.976
 
 
 
 
 
 
 
 
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  ---
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- # Dataset Card for "acl-x-ray"
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- [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  names:
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  '0': acl-x-ray
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  '1': acl
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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: acl-x-ray
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+ tags:
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+ - rf100
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  ---
 
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+ # Dataset Card for acl-x-ray
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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/acl-x-ray
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+ - **Point of Contact:** francesco.zuppichini@gmail.com
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+
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+ ### Dataset Summary
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+
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+ acl-x-ray
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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/acl-x-ray
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+
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+ ### Citation Information
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+
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+ ```
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+ @misc{ acl-x-ray,
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+ title = { acl x ray 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/acl-x-ray } },
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+ url = { https://universe.roboflow.com/object-detection/acl-x-ray },
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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.