image_id
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image
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width
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height
int64
objects
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pixel_values
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labels
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0
350
300
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7
1,024
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9
600
600
{ "area": [ 239096 ], "bbox": [ [ 69, 11, 418, 572 ] ], "category": [ 6 ] }
[[[-2.1179039478302,-2.1179039478302,-2.1179039478302,-2.1179039478302,-2.1179039478302,-2.117903947(...TRUNCATED)
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{"area":[239096.0],"boxes":[[0.5366666913032532,0.49500003457069397,0.6966666579246521,0.95333337783(...TRUNCATED)

This Dataset is created from processing the files from this GitHub repository : PlantDoc-Object-Detection-Dataset

Citation

BibTeX:

@inproceedings{10.1145/3371158.3371196,
author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},
title = {PlantDoc: A Dataset for Visual Plant Disease Detection},
year = {2020},
isbn = {9781450377386},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3371158.3371196},
doi = {10.1145/3371158.3371196},
booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},
pages = {249–253},
numpages = {5},
keywords = {Deep Learning, Object Detection, Image Classification},
location = {Hyderabad, India},
series = {CoDS COMAD 2020}
}
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