
# Dataset Card for cloud-types

** The original COCO dataset is stored at `dataset.tar.gz`**

## Dataset Description

- **Homepage:** https://universe.roboflow.com/object-detection/cloud-types
- **Point of Contact:** francesco.zuppichini@gmail.com

### Dataset Summary

cloud-types

### 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/cloud-types

### Citation Information

```
@misc{ cloud-types,
    title = { cloud types Dataset },
    type = { Open Source Dataset },
    author = { Roboflow 100 },
    howpublished = { \url{ https://universe.roboflow.com/object-detection/cloud-types } },
    url = { https://universe.roboflow.com/object-detection/cloud-types },
    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.
task_categories:object-detection annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:en license:cc rf100 region:us
{"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": "cloud-types", "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": "cloud-types", "1": "Fish", "2": "Flower", "3": "Gravel", "4": "Sugar"}}}}]}]}, "tags": ["rf100"]}
TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us 


# Dataset Card for cloud-types

 The original COCO dataset is stored at 'URL'

## Dataset Description

- Homepage: URL
- Point of Contact: francesco.zuppichini@URL

### Dataset Summary

cloud-types

### 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.



### 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 format)
  - 'category': the object's category.


#### Who are the annotators?

Annotators are Roboflow users

## Additional Information

### Licensing Information

See original homepage URL





### Contributions

Thanks to @mariosasko for adding this dataset.
# Dataset Card for cloud-types

 The original COCO dataset is stored at 'URL' ## Dataset Description

- Homepage: URL
- Point of Contact: francesco.zuppichini@URL ### Dataset Summary

cloud-types ### 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. ### 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 format)
  - 'category': the object's category. #### Who are the annotators?

Annotators are Roboflow users ## Additional Information ### Licensing Information

See original homepage URL ### Contributions

Thanks to @mariosasko for adding this dataset.
TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us 
 # Dataset Card for cloud-types

 The original COCO dataset is stored at 'URL' ## Dataset Description

- Homepage: URL
- Point of Contact: francesco.zuppichini@URL ### Dataset Summary

cloud-types ### 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. ### 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 format)
  - 'category': the object's category. #### Who are the annotators?

Annotators are Roboflow users ## Additional Information ### Licensing Information

See original homepage URL ### Contributions

Thanks to @mariosasko for adding this dataset.
passage: TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us 
# Dataset Card for cloud-types

 The original COCO dataset is stored at 'URL'## Dataset Description

- Homepage: URL
- Point of Contact: francesco.zuppichini@URL### Dataset Summary

cloud-types### 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.### 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 format)
  - 'category': the object's category.#### Who are the annotators?

Annotators are Roboflow users## Additional Information### Licensing Information

See original homepage URL### Contributions

Thanks to @mariosasko for adding this dataset.