Datasets:

Francesco
/

sedimentary-features-9eosf

image_id int64 0 155	width int32 640 640	height int32 640 640	objects sequence
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128	640	640	{ "id": [ 2614, 2615, 2616, 2617, 2618, 2619, 2620, 2621, 2622, 2623, 2624, 2625, 2626, 2627, 2628, 2629, 2630 ], "area": [ 2570, 2860, 3504, 2994, 3128, 1467, 2103, 3050, 3406, 2367, 5238, 1704, ...
111	640	640	{ "id": [ 2242, 2243, 2244, 2245 ], "area": [ 3827, 1669, 3063, 1872 ], "bbox": [ [ 407, 471, 43, 89 ], [ 477, 495, 53, 31.5 ], [ 412, 153, 47.5, 64.5 ], [ 330, 161, ...
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77	640	640	{ "id": [ 1708, 1709, 1710, 1711 ], "area": [ 2366, 3118, 2817, 1468 ], "bbox": [ [ 3, 73, 84.5, 28 ], [ 8, 353, 81, 38.5 ], [ 325, 461, 80.5, 35 ], [ 106, 347, ...
126	640	640	{ "id": [ 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564, 2565, 2566, 2567, 2568, 2569, 2570, 2571, 2572, 2573, 2574, 2575, 2576, 2577, 2578, 2579, 2580, 2581, 2582, 2583, 2584, 2585, 2586, 2587...
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94	640	640	{ "id": [ 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955 ], "area": [ 2186, 1334, 1771, 1344, 6935, 4655, 3873, 5986, 4462, 5256, 2887, 3237, 4477, 3832, ...
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149	640	640	{ "id": [ 3057, 3058, 3059, 3060, 3061, 3062, 3063, 3064, 3065, 3066, 3067 ], "area": [ 1023, 793, 4628, 2835, 2549, 2646, 3255, 2299, 7726, 3218, 1291 ], "bbox": [ [ 285, 354, 31, 33 ], ...
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63	640	640	{ "id": [ 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375 ], "area": [ 5183, 7611, 3126, 4856, 2562, 1538, 1282, 3334, ...
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End of preview. Expand in Data Studio

Dataset Card for sedimentary-features-9eosf

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

Dataset Summary

sedimentary-features-9eosf

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/sedimentary-features-9eosf

Citation Information

@misc{ sedimentary-features-9eosf,
    title = { sedimentary features 9eosf Dataset },
    type = { Open Source Dataset },
    author = { Roboflow 100 },
    howpublished = { \url{ https://universe.roboflow.com/object-detection/sedimentary-features-9eosf } },
    url = { https://universe.roboflow.com/object-detection/sedimentary-features-9eosf },
    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: 11

Homepage:

universe.roboflow.com

Point of Contact:

francesco.zuppichini@gmail.com

Size of downloaded dataset files:

17.2 MB

Size of the auto-converted Parquet files:

17.2 MB

Number of rows:

222