Datasets:
dataset_info:
features:
- name: image
dtype: image
- name: labels
sequence:
class_label:
names:
'0': complex
'1': frog_eye_leaf_spot
'2': healthy
'3': powdery_mildew
'4': rust
'5': scab
- name: label_names
sequence: string
- name: image_id
dtype: string
splits:
- name: train
num_bytes: 14557242028.669252
num_examples: 16768
- name: validation
num_bytes: 1603451702.490748
num_examples: 1864
download_size: 16094435250
dataset_size: 16160693731.16
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
license: apache-2.0
task_categories:
- image-classification
tags:
- multi-label
pretty_name: PlantPathology-Challenge-2021-FGVC8
size_categories:
- 10K<n<100K
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.