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README.md

@@ -25,19 +25,115 @@ dataset_info:
             '0': axial-MRI
             '1': negative
             '2': positive
-  splits:
-  - name: train
-    num_bytes: 4395159.0
-    num_examples: 253
-  - name: validation
-    num_bytes: 669879.0
-    num_examples: 39
-  - name: test
-    num_bytes: 1166160.0
-    num_examples: 79
-  download_size: 4786695
-  dataset_size: 6231198.0
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+language:
+- en
+license:
+- cc
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- object-detection
+task_ids: []
+pretty_name: axial-mri
+tags:
+- rf100
 ---
-# Dataset Card for "axial-mri"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# Dataset Card for axial-mri
+
+** The original COCO dataset is stored at `dataset.tar.gz`**
+
+## Dataset Description
+
+- **Homepage:** https://universe.roboflow.com/object-detection/axial-mri
+- **Point of Contact:** [email protected]
+
+### Dataset Summary
+
+axial-mri
+
+### Supported Tasks and Leaderboards
+
+- `object-detection`: The dataset can be used to train a model for Object Detection.
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A data point comprises an image and its object annotations.
+
+```
+{
+  'image_id': 15,
+  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
+  'width': 964043,
+  'height': 640,
+  'objects': {
+    'id': [114, 115, 116, 117], 
+    'area': [3796, 1596, 152768, 81002],
+    'bbox': [
+      [302.0, 109.0, 73.0, 52.0],
+      [810.0, 100.0, 57.0, 28.0],
+      [160.0, 31.0, 248.0, 616.0],
+      [741.0, 68.0, 202.0, 401.0]
+    ], 
+    'category': [4, 4, 0, 0]
+  }
+}
+```
+
+### Data Fields
+
+- `image`: the image id
+- `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]`
+- `width`: the image width
+- `height`: the image height
+- `objects`: a dictionary containing bounding box metadata for the objects present on the image
+  - `id`: the annotation id
+  - `area`: the area of the bounding box
+  - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
+  - `category`: the object's category.
+
+
+#### Who are the annotators?
+
+Annotators are Roboflow users
+
+## Additional Information
+
+### Licensing Information
+
+See original homepage https://universe.roboflow.com/object-detection/axial-mri
+
+### Citation Information
+
+```
+@misc{ axial-mri,
+    title = { axial mri Dataset },
+    type = { Open Source Dataset },
+    author = { Roboflow 100 },
+    howpublished = { \url{ https://universe.roboflow.com/object-detection/axial-mri } },
+    url = { https://universe.roboflow.com/object-detection/axial-mri },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { nov },
+    note = { visited on 2023-03-29 },
+}"
+```
+
+### Contributions
+
+Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.