Update README.md

README.md

@@ -41,4 +41,25 @@ tags:
 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:
+  - https://www.kaggle.com/c/plant-pathology-2021-fgvc8
+  - https://www.kaggle.com/c/plant-pathology-2021-fgvc8
+
+## 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.
+```