Datasets:
image imagewidth (px) 2.59k 5.18k | labels sequencelengths 1 3 | label_names sequencelengths 1 3 | image_id stringlengths 16 16 |
|---|---|---|---|
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[
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[
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Description
Dataset from the Plant Pathology 2021 (FGVC8) Challenge.
' For Plant Pathology 2021-FGVC8, we have significantly increased the number of foliar disease images and added additional disease categories. This year’s dataset contains approximately 23,000 high-quality RGB images of apple foliar diseases, including a large expert-annotated disease dataset. This dataset reflects real field scenarios by representing non-homogeneous backgrounds of leaf images taken at different maturity stages and at different times of day under different focal camera settings. '
The original dataset has one train split and a test split that was hidden for the challenge. I have taken 10% of train for a validation, using stratified sampling. I do not have access to the test samples.
- Website:
Usage
This dataset is serving as a canonical example for multi-label image classificatino datasets with timm. The additions to train & val scripts for this are a WIP...
Citation
Thapa, Ranjita, Zhang, Kai, Snavely, Noah, Belongie, Serge, and Khan, Awais. Plant Pathology 2021 - FGVC8.
https://kaggle.com/competitions/plant-pathology-2021-fgvc8, 2021. Kaggle.
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