image_id
int32
1
13
| image
image
| class_segmentation
image
| object_segmentation
image
| shapes
large_string
|
---|---|---|---|---|
1 | "[{'type': 'points', 'label': 'plant_center', 'points': (111.69, 227.25)}, {'type': 'points', 'label': 'plant_center', 'points': (77.59, 128.12)}, {'type': 'points', 'label': 'plant_center', 'points': (55.23, 31.58)}, {'type': 'polygon', 'label': 'greenery', 'points': [(149.07, 0.0), (149.68, 1.23), (138.1, 20.78), (135.92, 24.4), (126.51, 33.82), (125.06, 35.99), (122.16, 37.44), (124.34, 41.06), (127.23, 35.99), (130.85, 33.82), (137.37, 31.64), (143.16, 33.09), (146.06, 38.16), (147.51, 44.68)..." |
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2 | "[{'type': 'polygon', 'label': 'First', 'points': [(364.9, 136.5), (369.0, 121.0), (360.0, 115.0), (340.0, 110.0), (338.7, 101.6), (332.2, 100.7), (328.0, 110.0), (324.0, 111.0), (320.0, 107.0), (308.3, 103.5), (304.0, 110.0), (292.0, 103.2), (286.0, 112.0), (282.0, 120.0), (266.9, 123.9), (279.0, 142.0), (279.0, 147.0), (296.5, 154.5), (313.3, 155.2), (330.0, 164.0), (342.0, 162.0), (346.0, 155.0), (356.72, 156.09), (362.53, 150.47)]}, {'type': 'polygon', 'label': 'First', 'points': [(453.93, 32..." |
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3 | "[{'type': 'polygon', 'label': 'First', 'points': [(933.0, 485.0), (928.0, 485.0), (917.0, 491.0), (911.0, 499.0), (911.0, 505.0), (918.0, 517.0), (930.0, 523.0), (939.0, 521.0), (949.0, 505.0), (947.0, 493.0)]}, {'type': 'polygon', 'label': 'First', 'points': [(814.0, 427.0), (798.0, 433.0), (796.0, 437.0), (796.0, 452.0), (799.0, 458.0), (809.0, 467.0), (818.0, 468.0), (824.0, 465.0), (835.0, 450.0), (835.0, 443.0), (824.0, 429.0)]}, {'type': 'polygon', 'label': 'First', 'points': [(810.0, 485...." |
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4 | "[{'type': 'polygon', 'label': 'First', 'points': [(1514.0, 0.0), (1507.0, 16.0), (1508.0, 61.0), (1513.0, 88.0), (1521.0, 100.0), (1526.0, 120.0), (1526.0, 136.0), (1529.0, 146.0), (1540.0, 150.0), (1558.0, 150.0), (1580.0, 164.0), (1601.0, 166.0), (1619.0, 160.0), (1633.0, 149.0), (1638.0, 148.0), (1646.0, 139.0), (1646.0, 113.0), (1654.0, 86.0), (1653.0, 76.0), (1655.0, 63.0), (1653.0, 55.0), (1655.0, 44.0), (1667.0, 28.0), (1667.0, 20.0), (1670.0, 16.0), (1668.0, 15.0), (1659.0, 19.0), (1644...." |
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5 | "[{'type': 'polygon', 'label': 'First', 'points': [(1999.0, 63.0), (1987.0, 68.0), (1970.0, 69.0), (1969.0, 76.0), (1978.0, 85.0), (1999.0, 87.0)]}, {'type': 'polygon', 'label': 'First', 'points': [(1969.0, 0.0), (1973.0, 20.0), (1969.0, 28.0), (1975.0, 36.0), (1982.0, 40.0), (1991.0, 41.0), (1999.0, 45.0), (1999.0, 0.0)]}, {'type': 'polygon', 'label': 'First', 'points': [(0.0, 446.9), (0.0, 694.0), (15.0, 695.0), (18.0, 698.0), (20.0, 709.0), (24.0, 716.0), (23.0, 723.0), (11.0, 735.0), (1.0, 73..." |
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6 | "[{'type': 'polygon', 'label': 'greenery', 'points': [(493.8, 76.32), (495.72, 76.2), (498.12, 75.96), (501.48, 75.24), (503.88, 74.76), (506.76, 76.08), (509.64, 77.76), (510.12, 81.6), (509.64, 84.84), (509.04, 87.24), (514.2, 87.84), (516.36, 90.84), (519.48, 93.72), (521.04, 96.84), (521.88, 99.36), (520.68, 102.96), (516.84, 106.2), (512.28, 106.8), (508.56, 106.8), (506.28, 109.56), (503.04, 114.0), (499.08, 116.28), (495.72, 116.4), (491.64, 116.4), (487.44, 111.36), (487.44, 108.24), (490..." |
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7 | "[{'type': 'polygon', 'label': 'First', 'points': [(131.0, 304.0), (125.0, 312.0), (127.0, 320.0), (136.0, 332.0), (143.0, 332.0), (147.0, 328.0), (150.0, 319.0), (157.0, 319.0), (158.0, 313.0), (156.0, 308.0), (148.0, 300.0), (138.82, 293.87), (135.22, 301.3)]}, {'type': 'polygon', 'label': 'First', 'points': [(26.0, 253.0), (16.0, 257.0), (11.0, 262.0), (13.0, 272.0), (21.0, 275.0), (27.0, 274.0), (31.0, 256.0)]}, {'type': 'polygon', 'label': 'First', 'points': [(45.0, 291.0), (40.0, 296.0), (4..." |
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8 | "[{'type': 'polygon', 'label': 'First', 'points': [(680.0, 0.0), (679.0, 10.0), (681.0, 13.0), (693.0, 17.0), (717.0, 17.0), (722.0, 22.0), (731.0, 21.0), (735.0, 24.0), (740.0, 37.0), (744.0, 40.0), (744.0, 44.0), (733.0, 59.0), (732.0, 70.0), (748.0, 104.0), (760.0, 116.0), (760.0, 122.0), (755.0, 132.0), (757.0, 137.0), (750.0, 144.0), (744.0, 156.0), (744.0, 170.0), (747.0, 176.0), (758.0, 186.0), (764.0, 189.0), (777.0, 188.0), (789.0, 204.0), (780.0, 220.0), (776.0, 238.0), (783.0, 248.0), ..." |
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9 | "[{'type': 'polygon', 'label': 'greenery', 'points': [(0.0, 313.46), (3.1, 313.6), (7.4, 312.4), (15.3, 311.3), (19.1, 309.0), (23.0, 303.9), (24.5, 299.5), (23.3, 292.1), (21.2, 287.9), (19.2, 285.8), (19.0, 281.1), (16.8, 275.2), (13.2, 273.8), (7.9, 272.6), (2.2, 271.8), (0.0, 272.36)]}, {'type': 'polygon', 'label': 'greenery', 'points': [(0.0, 194.35), (2.84, 196.3), (10.72, 200.04), (16.52, 199.0), (20.46, 202.11), (29.38, 204.6), (35.6, 203.15), (38.92, 197.96), (38.92, 192.16), (38.92, 185..." |
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10 | "[{'type': 'polygon', 'label': 'First', 'points': [(0.0, 456.0), (7.0, 452.0), (19.0, 452.0), (35.0, 447.0), (41.0, 447.0), (47.0, 452.0), (64.0, 449.0), (74.0, 453.0), (83.0, 450.0), (88.0, 452.0), (112.0, 453.0), (131.0, 450.0), (192.0, 451.0), (200.0, 445.0), (214.0, 449.0), (229.0, 443.0), (237.0, 450.0), (258.0, 449.0), (266.0, 451.0), (277.0, 445.0), (285.0, 447.0), (298.0, 447.0), (303.0, 445.0), (317.0, 450.0), (323.0, 447.0), (338.0, 447.0), (342.0, 445.0), (357.0, 450.0), (371.0, 447.0)..." |
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11 | "[{'type': 'polygon', 'label': 'First', 'points': [(96.0, 779.0), (72.0, 774.0), (58.0, 763.0), (48.0, 767.0), (28.0, 764.0), (14.0, 770.0), (9.0, 768.0), (1.0, 769.0), (0.0, 944.0), (4.0, 941.0), (20.0, 937.0), (24.0, 933.0), (28.0, 925.0), (28.0, 920.0), (24.0, 913.0), (26.0, 907.0), (59.0, 902.0), (82.0, 891.0), (96.0, 891.0), (108.0, 873.0), (109.0, 859.0), (116.0, 850.0), (120.0, 840.0), (116.0, 833.0), (116.0, 825.0), (108.0, 819.0), (107.0, 813.0), (114.0, 796.0)]}, {'type': 'polygon', 'la..." |
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12 | "[{'type': 'polygon', 'label': 'First', 'points': [(434.0, 1190.0), (428.0, 1198.0), (425.0, 1207.0), (418.0, 1215.0), (419.0, 1233.0), (428.0, 1248.0), (433.0, 1250.0), (446.0, 1250.0), (457.0, 1257.0), (466.0, 1275.0), (473.0, 1279.0), (489.0, 1282.0), (494.0, 1287.0), (495.0, 1293.0), (513.0, 1309.0), (537.0, 1314.0), (545.0, 1311.0), (554.0, 1303.0), (562.0, 1290.0), (565.0, 1278.0), (554.0, 1268.0), (551.0, 1252.0), (535.0, 1247.0), (535.0, 1243.0), (525.0, 1225.0), (506.0, 1214.0), (482.0, ..." |
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13 | "[{'type': 'polygon', 'label': 'First', 'points': [(124.0, 695.0), (110.0, 675.0), (89.0, 657.0), (83.0, 662.0), (82.0, 672.0), (84.0, 675.0), (82.0, 677.0), (66.0, 679.0), (55.0, 677.0), (48.0, 682.0), (36.0, 680.0), (30.0, 685.0), (3.0, 690.0), (0.0, 693.0), (0.0, 732.0), (10.0, 727.0), (25.0, 724.0), (45.0, 724.0), (52.0, 721.0), (56.0, 716.0), (67.0, 722.0), (76.0, 723.0), (80.0, 721.0), (80.0, 710.0), (84.0, 711.0), (86.0, 716.0), (100.0, 730.0), (106.0, 732.0), (111.0, 725.0), (113.0, 710.0..." |
Plantations Segmentation
The images consist of aerial photography of agricultural plantations with crops such as cabbage and zucchini. The dataset addresses agricultural tasks such as plant detection and counting, health assessment, and irrigation planning. The dataset consists of plantations' photographs with object and class segmentation of cabbage.
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This is just an example of the data
Leave a request on https://trainingdata.pro/data-market to discuss your requirements, learn about the price and buy the dataset.
Dataset structure
- Plantations_Segmentation - contains of original plantation images (folder img) and file with annotations (.xml)
- Object_Segmentation - includes object segmentation masks for the original images
- Class_Segmentation - includes class segmentation masks for the original images
Types of segmentation
The dataset includes two types of segmentation:
- Class Segmentation - objects corresponding to one class are identified
- Object Segmentation - all objects are identified separately
Data Format
Each image from img
folder is accompanied by an XML-annotation in the annotations.xml
file indicating the coordinates of the polygons. For each point, the x and y coordinates are provided.
Example of XML file structure
Plantation segmentation might be made in accordance with your requirements.
**TrainingData**
More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets
TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets
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