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Update parquet files
Browse files- dataset.tar.gz → Francesco--aerial-pool/parquet-test.parquet +2 -2
- data/test-00000-of-00001-80b6a5f91f90f19b.parquet → Francesco--aerial-pool/parquet-train.parquet +2 -2
- data/validation-00000-of-00001-4601a2b8889afc6a.parquet → Francesco--aerial-pool/parquet-validation.parquet +2 -2
- README.md +0 -142
- data/train-00000-of-00001-2cef666ef4acd79d.parquet +0 -3
- dataset_info.json +0 -1
dataset.tar.gz → Francesco--aerial-pool/parquet-test.parquet
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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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---
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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': aerial-pool
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'1': black-hat
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'2': bodysurface
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'3': bodyunder
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'4': umpire
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'5': white-hat
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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: aerial-pool
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tags:
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- rf100
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---
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# Dataset Card for aerial-pool
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** The original COCO dataset is stored at `dataset.tar.gz`**
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## Dataset Description
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- **Homepage:** https://universe.roboflow.com/object-detection/aerial-pool
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- **Point of Contact:** francesco.zuppichini@gmail.com
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### Dataset Summary
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aerial-pool
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### Supported Tasks and Leaderboards
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- `object-detection`: The dataset can be used to train a model for Object Detection.
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### Languages
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English
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## Dataset Structure
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### Data Instances
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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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'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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### Data Fields
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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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#### Who are the annotators?
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Annotators are Roboflow users
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## Additional Information
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### Licensing Information
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See original homepage https://universe.roboflow.com/object-detection/aerial-pool
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### Citation Information
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```
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@misc{ aerial-pool,
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title = { aerial pool 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/aerial-pool } },
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url = { https://universe.roboflow.com/object-detection/aerial-pool },
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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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### Contributions
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Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
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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}
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