Datasets:

Francesco
/

4-fold-defect

image_id int64 0 502	width int32 640 640	height int32 640 640	objects sequence
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19	640	640	{ "id": [ 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305 ], "area": [ 259, 263, 238, 263, 263, 196, 286, 193, ...
21	640	640	{ "id": [ 322 ], "area": [ 238 ], "bbox": [ [ 386, 280, 17, 14 ] ], "category": [ 1 ] }
495	640	640	{ "id": [ 9124, 9125, 9126, 9127, 9128, 9129, 9130, 9131, 9132, 9133, 9134, 9135, 9136, 9137, 9138, 9139, 9140, 9141, 9142, 9143, 9144, 9145, 9146, 9147, 9148, 9149, 9150, 9151, 9152, 9153, 9154...
109	640	640	{ "id": [ 1548, 1549, 1550, 1551, 1552, 1553 ], "area": [ 263, 238, 217, 240, 370, 286 ], "bbox": [ [ 31, 153, 15.5, 17 ], [ 471, 28, 17, 14 ], [ 546, 369, 14, 15.5 ]...
425	640	640	{ "id": [ 7792, 7793, 7794, 7795, 7796 ], "area": [ 529, 494, 563, 563, 460 ], "bbox": [ [ 305, 336, 23, 23 ], [ 230, 412, 23, 21.5 ], [ 388, 519, 23, 24.5 ], [ 34,...
60	640	640	{ "id": [ 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941 ], "area": [ 240, 156, 137, 217, 121, 137, 196, 121, 156, 175, 193, 156, 121 ], "bbox": [ [ 563, 197, 15.5, ...
263	640	640	{ "id": [ 4741, 4742, 4743, 4744, 4745, 4746, 4747, 4748, 4749, 4750, 4751, 4752, 4753 ], "area": [ 121, 121, 121, 121, 81, 121, 67, 121, 121, 137, 81, 121, 156 ], "bbox": [ [ 231, 191, ...
253	640	640	{ "id": [ 4510, 4511, 4512, 4513, 4514 ], "area": [ 400, 430, 340, 370, 196 ], "bbox": [ [ 400, 153, 20, 20 ], [ 298, 194, 21.5, 20 ], [ 429, 449, 20, 17 ], [ 457, ...

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Dataset Card for 4-fold-defect

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

Dataset Summary

4-fold-defect

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 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/4-fold-defect

Citation Information

@misc{ 4-fold-defect,
    title = { 4 fold defect Dataset },
    type = { Open Source Dataset },
    author = { Roboflow 100 },
    howpublished = { \url{ https://universe.roboflow.com/object-detection/4-fold-defect } },
    url = { https://universe.roboflow.com/object-detection/4-fold-defect },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { nov },
    note = { visited on 2023-03-29 },
}"

Contributions

Thanks to @mariosasko for adding this dataset.

Downloads last month: 153

Homepage:

universe.roboflow.com

Point of Contact:

francesco.zuppichini@gmail.com

Size of downloaded dataset files:

29.9 MB

Size of the auto-converted Parquet files:

29.9 MB

Number of rows:

670