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]
} | |
Batch1 | {
"bbox": [
[
140.34976,
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492.44224,
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]
],
"categories": [
4
]
} | |
Batch1 | {
"bbox": [
[
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0,
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],
[
577.53984,
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]
],
"categories": [
4,
4
]
} | |
Batch1 | {
"bbox": [
[
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],
[
0,
134.81519999999998,
109.05984000000001,
338.10335999999995
]
],
"categories": [
4,
4
]
} | |
Batch1 | {
"bbox": [
[
139.45279999999997,
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391.92064000000005,
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],
[
543.4665600000001,
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96.53312,
435.43968
]
],
"categories": [
4,
4
]
} | |
Batch1 | {
"bbox": [
[
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[
0,
0,
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[
568.58752,
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[
... | |
Batch1 | {
"bbox": [
[
442.77248,
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38.07616,
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],
[
617.55328,
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[
544.1126399999999,
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[
569.39456,
0,
18.69696,
13... |
End of preview. Expand in Data Studio
Coffee Detection
A dataset for object detection of coffee beans. The dataset contains 3,254 images with 126,840 bounding box annotations across 5 categories.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{sanya2024coffee,
title={Coffee and cashew nut dataset: A dataset for detection, classification, and yield estimation for machine learning applications},
author={Sanya, Rahman and Nabiryo, Ann Lisa and Tusubira, Jeremy Francis and Murindanyi, Sudi and Katumba, Andrew and Nakatumba-Nabende, Joyce},
journal={Data in Brief},
volume={52},
pages={109952},
year={2024},
publisher={Elsevier}
}
Nakatumba-Nabende, Joyce; Katumba, Andrew; Sanya, Rahman; Tusubira, Jeremy; Murindanyi, Sudi; Namanya, Gloria; Nabiryo, Ann (2023), “Coffee and Cashew Nut Dataset”, Mendeley Data, V1, doi: 10.17632/r46c6bpfpf.1
This dataset was reformatted from its original format to match HuggingFace standards.
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