Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods
Paper • 2105.07660 • Published
Datasets:
image imagewidth (px) 1.02k 1.02k | domain stringclasses 18
values | country stringclasses 5
values | location stringclasses 9
values | development_stage stringclasses 4
values | objects dict |
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Arvalis_11 | France | VLB | Filling | {
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[
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252,
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Arvalis_3 | France | Gréoux | Filling - Ripening | {
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[
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[
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574,
8... | |
ETHZ_1 | Switzerland | Usask | Filling | {
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[
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[
339,
488,
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Arvalis_2 | France | Gréoux | Filling | {
"boxes": [
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[
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[
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[
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[
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181,
58... | |
NMBU_1 | Norway | NMBU | Filling | {
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[
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[
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37,
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Arvalis_2 | France | Gréoux | Filling | {
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[
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[
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Rres_1 | UK | Rothamsted | Filling - Ripening | {
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[
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110,
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[
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ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
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[
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[
472,
988,
30,... | |
ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
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[
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[
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[
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[
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400,
87,
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[
461,
218,
106,
... | |
Arvalis_4 | France | Gréoux | Filling | {
"boxes": [
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52,
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[
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104,
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[
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[
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136,
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[
38,
272,
102,
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[
869,
213,
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ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
[
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[
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[
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[
114,
841,
59,
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[
177,
252,
75,
33
],
[
301,
83,
6... | |
ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
[
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[
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874,
126,
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[
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989,
114,
35
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[
573,
348,
70,... | |
ULiège-GxABT_1 | Belgium | Gembloux | Ripening | {
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[
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[
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[
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78,
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[
694,
249,
60,
114
],
[
702,
290,
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Rres_1 | UK | Rothamsted | Filling - Ripening | {
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[
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[
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[
0,
817,
58,
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[
614,
744,
69,... | |
ULiège-GxABT_1 | Belgium | Gembloux | Ripening | {
"boxes": [
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[
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[
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[
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74,
180
],
[
316,
164,
112,
94
],
[
352,
150,
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Arvalis_2 | France | Gréoux | Filling | {
"boxes": [
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[
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[
139,
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[
362,
296,
64,
44
],
[
392,
88,
120,
71
],
[
814,
112,
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Rres_1 | UK | Rothamsted | Filling - Ripening | {
"boxes": [
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[
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[
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[
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[
408,
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75,
77
],
[
633,
501,
... | |
Arvalis_6 | France | VSC | Filling - Ripening | {
"boxes": [
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[
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145,
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[
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[
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187,
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],
[
355,
174,
39,
76
],
[
197,
332,
... | |
Arvalis_6 | France | VSC | Filling - Ripening | {
"boxes": [
[
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[
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66,
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[
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100,
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[
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232,
38,
139
],
[
445,
550,
54,
61
],
[
366,
12,
89... | |
Arvalis_2 | France | Gréoux | Filling | {
"boxes": [
[
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105,
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[
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79,
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],
[
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[
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92,
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],
[
149,
530,
81,
78
],
[
410,
479,
98... | |
Inrae_1 | France | Toulouse | Filling - Ripening | {
"boxes": [
[
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235
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[
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63,
117
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[
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[
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113,
129
],
[
12,
724,
223,
258
],
[
452,
524,
... | |
ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
[
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[
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62,
134
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[
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[
843,
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87,
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],
[
547,
426,
129,
42
],
[
245,
674,
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Arvalis_6 | France | VSC | Filling - Ripening | {
"boxes": [
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[
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[
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108,
112
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[
729,
369,
155,
61
],
[
291,
512,
182,
69
],
[
711,
115,
17... | |
ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
[
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33,
35,
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],
[
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506,
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119
],
[
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[
780,
50,
81,
111
],
[
36,
719,
115,
130
],
[
431,
959,
... | |
Arvalis_1 | France | Gréoux | Post-flowering | {
"boxes": [
[
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102,
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[
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41,
61
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[
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42,
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],
[
116,
690,
48,
55
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[
82,
700,
56,
43
],
[
240,
73,
72... | |
Arvalis_4 | France | Gréoux | Filling | {
"boxes": [
[
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62,
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],
[
108,
861,
42,
32
],
[
951,
120,
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],
[
955,
88,
52,
42
],
[
830,
531,
64,
69
],
[
112,
778,
41... | |
Arvalis_6 | France | VSC | Filling - Ripening | {
"boxes": [
[
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36
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[
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[
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[
256,
370,
88,
53
],
[
230,
905,
61,
119
],
[
605,
165,
97... | |
Rres_1 | UK | Rothamsted | Filling - Ripening | {
"boxes": [
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[
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[
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62,
69,
128
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[
848,
386,
86,
88
],
[
75,
81,
17... | |
Arvalis_3 | France | Gréoux | Filling - Ripening | {
"boxes": [
[
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67,
72
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[
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562,
131,
165
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[
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[
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77,
112
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[
782,
792,
113,
192
],
[
533,
184,
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Arvalis_5 | France | VLB | Filling | {
"boxes": [
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[
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54,
51
],
[
846,
523,
71,
59
],
[
0,
166,
77,
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Rres_1 | UK | Rothamsted | Filling - Ripening | {
"boxes": [
[
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56
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[
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807,
83,
71
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[
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[
152,
88,
68,
62
],
[
805,
978,
73,
46
],
[
375,
779,
97,... | |
Arvalis_5 | France | VLB | Filling | {
"boxes": [
[
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29,
79
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[
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47,
52
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[
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[
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450,
138,
64
],
[
793,
913,
87,
87
],
[
852,
384,
... | |
Arvalis_5 | France | VLB | Filling | {
"boxes": [],
"categories": []
} | |
Arvalis_5 | France | VLB | Filling | {
"boxes": [],
"categories": []
} | |
Arvalis_6 | France | VSC | Filling - Ripening | {
"boxes": [
[
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499,
113,
133
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[
481,
673,
95,
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],
[
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309,
111,
103
],
[
178,
715,
72,
58
],
[
666,
375,
126,
114
],
[
567,
373,
... | |
Arvalis_6 | France | VSC | Filling - Ripening | {
"boxes": [
[
573,
76,
72,
82
],
[
488,
483,
88,
75
],
[
445,
903,
54,
121
],
[
145,
151,
185,
99
],
[
34,
327,
243,
123
],
[
893,
737,
... | |
ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
[
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39,
51
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[
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307,
75,
30
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[
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103,
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[
603,
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55,
36
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[
127,
834,
84,
39
],
[
466,
16,
1... | |
Arvalis_6 | France | VSC | Filling - Ripening | {
"boxes": [
[
394,
145,
130,
159
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[
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141,
67
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[
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981,
53,
42
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[
893,
673,
67,
82
],
[
728,
366,
84,
120
],
[
399,
862,
... | |
Arvalis_4 | France | Gréoux | Filling | {
"boxes": [
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142,
71
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[
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91,
72
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[
854,
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[
375,
20,
162,
82
],
[
783,
865,
138,
83
],
[
28,
268,
9... | |
ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
[
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55,
62
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[
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0,
45,
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],
[
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[
537,
513,
50,
102
],
[
6,
937,
54,
51
],
[
64,
135,
93,
... | |
Rres_1 | UK | Rothamsted | Filling - Ripening | {
"boxes": [
[
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70,
60
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[
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178,
70,
56
],
[
698,
483,
61,
68
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[
793,
467,
77,
68
],
[
567,
831,
68,
84
],
[
421,
963,
7... | |
Rres_1 | UK | Rothamsted | Filling - Ripening | {
"boxes": [
[
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122,
141,
130
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[
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40,
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],
[
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118
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[
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703,
64,
95
],
[
930,
949,
64,
50
],
[
104,
669,
... | |
Rres_1 | UK | Rothamsted | Filling - Ripening | {
"boxes": [
[
561,
949,
74,
72
],
[
634,
705,
73,
61
],
[
346,
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64
],
[
471,
599,
64,
89
],
[
241,
392,
106,
85
],
[
681,
425,
... | |
Arvalis_3 | France | Gréoux | Filling - Ripening | {
"boxes": [
[
38,
46,
81,
93
],
[
828,
840,
71,
85
],
[
1001,
467,
23,
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[
52,
615,
67,
164
],
[
220,
895,
89,
82
],
[
378,
111,
67... | |
Inrae_1 | France | Toulouse | Filling - Ripening | {
"boxes": [
[
540,
949,
117,
72
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[
347,
390,
72,
78
],
[
26,
993,
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31
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[
97,
0,
107,
48
],
[
757,
0,
82,
66
],
[
665,
399,
118,
... | |
Arvalis_5 | France | VLB | Filling | {
"boxes": [
[
492,
889,
56,
50
],
[
286,
504,
80,
79
],
[
168,
22,
43,
78
],
[
454,
772,
60,
73
],
[
623,
877,
70,
72
],
[
664,
356,
40... | |
ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
[
959,
322,
65,
99
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[
754,
448,
73,
64
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[
647,
339,
67,
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[
496,
339,
99,
78
],
[
636,
76,
87,
78
],
[
590,
868,
79... | |
Inrae_1 | France | Toulouse | Filling - Ripening | {
"boxes": [
[
144,
439,
82,
93
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[
360,
91,
217,
99
],
[
360,
617,
166,
140
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[
0,
798,
51,
141
],
[
87,
0,
106,
72
],
[
44,
164,
87,... | |
Arvalis_7 | France | VLB | Filling - Ripening | {
"boxes": [
[
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354,
99,
83
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[
140,
339,
101,
49
],
[
132,
123,
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148
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[
203,
105,
101,
121
],
[
345,
196,
97,
95
],
[
442,
188,
... | |
ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
[
853,
4,
75,
79
],
[
694,
32,
85,
86
],
[
356,
806,
70,
58
],
[
906,
397,
94,
47
],
[
54,
506,
84,
50
],
[
91,
789,
66,
... | |
Rres_1 | UK | Rothamsted | Filling - Ripening | {
"boxes": [
[
589,
375,
61,
74
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[
324,
877,
81,
114
],
[
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349,
74,
71
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[
403,
82,
71,
68
],
[
179,
108,
55,
61
],
[
387,
293,
8... | |
ETHZ_1 | Switzerland | Usask | Filling | {
"boxes": [
[
502,
906,
51,
33
],
[
505,
690,
73,
45
],
[
361,
673,
98,
41
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[
933,
872,
50,
71
],
[
808,
2,
75,
79
],
[
166,
151,
60,... | |
Rres_1 | UK | Rothamsted | Filling - Ripening | {
"boxes": [
[
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188,
139
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[
473,
614,
130,
129
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[
620,
0,
76,
176
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[
571,
881,
134,
137
],
[
466,
293,
102,
242
],
[
660,
134,
... | |
Inrae_1 | France | Toulouse | Filling - Ripening | {
"boxes": [
[
975,
456,
49,
86
],
[
335,
76,
108,
108
],
[
23,
844,
145,
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[
491,
599,
97,
102
],
[
293,
274,
90,
87
],
[
721,
399,
... | |
Arvalis_5 | France | VLB | Filling | {
"boxes": [
[
401,
20,
157,
82
],
[
0,
481,
72,
103
],
[
908,
371,
102,
93
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[
194,
896,
120,
92
],
[
811,
120,
100,
77
],
[
204,
266,
... | |
Arvalis_2 | France | Gréoux | Filling | {
"boxes": [
[
738,
530,
78,
52
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[
405,
156,
49,
46
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[
385,
966,
61,
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[
0,
99,
64,
56
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[
144,
822,
52,
59
],
[
254,
4,
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End of preview. Expand in Data Studio
Dataset Card for "Global Wheat Head Dataset 2021" 😊
If you want any update on the Global Wheat Dataset Community, go on https://www.global-wheat.com/
Introduction
Wheat is essential for a large part of humanity. The "Global Wheat Head Dataset 2021" aims to support the development of deep learning models for wheat head detection. This dataset addresses challenges like overlapping plants and varying conditions across global wheat fields. It's a step towards automating plant phenotyping and enhancing agricultural practices. 🌾
Dataset Composition
- Images: Over 6000, Resolution - 1024x1024 pixels
- Annotations: 300k+ unique wheat heads with bounding boxes
- Geographic Coverage: Images from 11 countries
- Domains: Various, including sensor types and locations
- Splits: Training (Europe & Canada), Test (Other regions)
Dataset Composition
Files and Structure
- Images: Folder containing all images (
.png) - CSV Files:
competition_train.csv,competition_val.csv,competition_test.csvfor different dataset splits - Metadata:
Metadata.csvwith additional details
Labels
- Format: CSV with columns - image_name, BoxesString, domain
- BoxesString:
[x_min,y_min, x_max,y_max]format for bounding boxes - Domain: Specifies the image domain
Usage
Tutorials and Resources
- Tutorials available at AIcrowd Challenge Page
License
- Type: Creative Commons Attribution 4.0 International (cc-by-4.0)
- Details: Free to use with attribution
Citation
If you use this dataset in your research, please cite the following:
@article{david2020global,
title={Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods},
author={David, Etienne and others},
journal={Plant Phenomics},
volume={2020},
year={2020},
publisher={Science Partner Journal}
}
@misc{david2021global,
title={Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods},
author={Etienne David and others},
year={2021},
eprint={2105.07660},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Acknowledgements
Special thanks to all the contributors, researchers, and institutions that played a pivotal role in the creation of this dataset. Your efforts are helping to advance the field of agricultural sciences and technology. 👏
- Downloads last month
- 25
Creators
: Etienne David and others
Published
: July 12, 2021 | Version 1.0
Availability
Zenodo Link
Keywords
: Deep Learning, Wheat Counting, Plant Phenotyping
Number of rows:
6,512
Total file size:
1.26 GB