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Create README.md

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