Create README.md

README.md

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+---
+license: apache-2.0
+metrics:
+- accuracy
+- f1
+---
+Predicts plant type given an image.
+
+See https://www.kaggle.com/code/dima806/30-plant-types-image-detection-vit for more details.
+
+```
+Classification report:
+
+               precision    recall  f1-score   support
+
+        guava     0.9846    0.9600    0.9722       200
+     galangal     0.9418    0.8900    0.9152       200
+      bilimbi     0.9949    0.9750    0.9848       200
+        paddy     0.9731    0.9050    0.9378       200
+     eggplant     0.9848    0.9700    0.9773       200
+     cucumber     0.9561    0.9800    0.9679       200
+      cassava     0.9899    0.9800    0.9849       200
+       papaya     0.9851    0.9950    0.9900       200
+       banana     0.9950    0.9900    0.9925       200
+       orange     0.9534    0.9200    0.9364       200
+   cantaloupe     0.5271    0.3400    0.4134       200
+      coconut     0.9950    1.0000    0.9975       200
+     soybeans     0.9754    0.9900    0.9826       200
+       pomelo     0.9563    0.9850    0.9704       200
+    pineapple     0.9703    0.9800    0.9751       200
+        melon     0.5000    0.6150    0.5516       200
+      shallot     0.9949    0.9750    0.9848       200
+   peperchili     0.9755    0.9950    0.9851       200
+      spinach     0.9231    0.9600    0.9412       200
+      tobacco     0.9151    0.9700    0.9417       200
+     aloevera     0.9949    0.9800    0.9874       200
+      curcuma     0.9005    0.8600    0.8798       200
+         corn     0.9610    0.9850    0.9728       200
+       ginger     0.8551    0.8850    0.8698       200
+sweetpotatoes     1.0000    0.9950    0.9975       200
+         kale     0.9268    0.9500    0.9383       200
+    longbeans     0.9850    0.9850    0.9850       200
+   watermelon     0.9252    0.9900    0.9565       200
+        mango     0.9239    0.9100    0.9169       200
+   waterapple     0.8807    0.9600    0.9187       200
+
+     accuracy                         0.9292      6000
+    macro avg     0.9282    0.9292    0.9275      6000
+ weighted avg     0.9282    0.9292    0.9275      6000
+```