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data/test-00000-of-00001-80b6a5f91f90f19b.parquet → Francesco--aerial-pool/parquet-train.parquet

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data/validation-00000-of-00001-4601a2b8889afc6a.parquet → Francesco--aerial-pool/parquet-validation.parquet

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README.md

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----
-dataset_info:
-  features:
-  - name: image_id
-    dtype: int64
-  - name: image
-    dtype: image
-  - name: width
-    dtype: int32
-  - name: height
-    dtype: int32
-  - name: objects
-    sequence:
-    - name: id
-      dtype: int64
-    - name: area
-      dtype: int64
-    - name: bbox
-      sequence: float32
-      length: 4
-    - name: category
-      dtype:
-        class_label:
-          names:
-            '0': aerial-pool
-            '1': black-hat
-            '2': bodysurface
-            '3': bodyunder
-            '4': umpire
-            '5': white-hat
-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: aerial-pool
-tags:
-- rf100
----
-
-# Dataset Card for aerial-pool
-
-** The original COCO dataset is stored at `dataset.tar.gz`**
-
-## Dataset Description
-
-- **Homepage:** https://universe.roboflow.com/object-detection/aerial-pool
-- **Point of Contact:** francesco.zuppichini@gmail.com
-
-### Dataset Summary
-
-aerial-pool
-
-### 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/aerial-pool
-
-### Citation Information
-
-```
-@misc{ aerial-pool,
-    title = { aerial pool Dataset },
-    type = { Open Source Dataset },
-    author = { Roboflow 100 },
-    howpublished = { \url{ https://universe.roboflow.com/object-detection/aerial-pool } },
-    url = { https://universe.roboflow.com/object-detection/aerial-pool },
-    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.

data/train-00000-of-00001-2cef666ef4acd79d.parquet

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dataset_info.json

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-{"description": "\naerial pool - v3 2022-09-14 1:18pm\n==============================\n\nThis dataset was exported via roboflow.com on September 15, 2022 at 8:19 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 946 images.\nAerial-pool 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{ aerial-pool,\n    title = { aerial pool Dataset },\n    type = { Open Source Dataset },\n    author = { Roboflow 100 },\n    howpublished = { \\url{ https://universe.roboflow.com/object-detection/aerial-pool } },\n    url = { https://universe.roboflow.com/object-detection/aerial-pool },\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/aerial-pool", "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": ["aerial-pool", "black-hat", "bodysurface", "bodyunder", "umpire", "white-hat"], "_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": 41165779, "num_examples": 673, "dataset_name": "dataset"}, "validation": {"name": "validation", "num_bytes": 5828831, "num_examples": 96, "dataset_name": "dataset"}, "test": {"name": "test", "num_bytes": 10561065, "num_examples": 177, "dataset_name": "dataset"}}, "download_checksums": {"https://huggingface.co/datasets/Francesco/aerial-pool/resolve/main/dataset.tar.gz": {"num_bytes": 57185847, "checksum": null}}, "download_size": 57185847, "dataset_size": 57555675, "size_in_bytes": 114741522}