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@@ -4,4 +4,62 @@ metrics:
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  - accuracy
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  - f1
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  ---
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- See https://www.kaggle.com/code/dima806/mammals-45-types-image-classification-vit for more details.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - accuracy
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  - f1
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  ---
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+ Returns a common mammal type given an image.
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+
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+ See https://www.kaggle.com/code/dima806/mammals-45-types-image-classification-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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+ african_elephant 1.0000 1.0000 1.0000 71
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+ alpaca 0.9200 0.9718 0.9452 71
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+ american_bison 1.0000 1.0000 1.0000 71
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+ anteater 0.9853 0.9437 0.9640 71
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+ arctic_fox 0.9286 0.9155 0.9220 71
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+ armadillo 0.9726 1.0000 0.9861 71
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+ baboon 0.9718 0.9718 0.9718 71
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+ badger 1.0000 0.9718 0.9857 71
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+ blue_whale 0.9710 0.9437 0.9571 71
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+ brown_bear 0.9722 0.9859 0.9790 71
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+ camel 0.9861 1.0000 0.9930 71
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+ dolphin 0.8974 0.9859 0.9396 71
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+ giraffe 0.9857 0.9718 0.9787 71
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+ groundhog 0.9714 0.9577 0.9645 71
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+ highland_cattle 0.9859 0.9859 0.9859 71
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+ horse 1.0000 0.9859 0.9929 71
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+ jackal 0.9577 0.9444 0.9510 72
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+ kangaroo 0.8415 0.9583 0.8961 72
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+ koala 0.9589 0.9859 0.9722 71
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+ manatee 0.9861 0.9861 0.9861 72
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+ mongoose 0.9483 0.7746 0.8527 71
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+ mountain_goat 0.9855 0.9577 0.9714 71
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+ opossum 1.0000 0.9577 0.9784 71
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+ orangutan 1.0000 1.0000 1.0000 71
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+ otter 1.0000 0.9577 0.9784 71
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+ polar_bear 0.9706 0.9296 0.9496 71
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+ porcupine 1.0000 0.9722 0.9859 72
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+ red_panda 0.9718 0.9718 0.9718 71
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+ rhinoceros 0.9859 0.9859 0.9859 71
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+ sea_lion 0.7600 0.8028 0.7808 71
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+ seal 0.8308 0.7500 0.7883 72
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+ snow_leopard 1.0000 1.0000 1.0000 71
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+ squirrel 0.9444 0.9577 0.9510 71
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+ sugar_glider 0.8554 1.0000 0.9221 71
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+ tapir 1.0000 1.0000 1.0000 71
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+ vampire_bat 1.0000 0.9861 0.9930 72
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+ vicuna 1.0000 0.8873 0.9403 71
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+ walrus 0.9342 0.9861 0.9595 72
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+ warthog 0.9571 0.9437 0.9504 71
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+ water_buffalo 0.9333 0.9859 0.9589 71
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+ weasel 0.9583 0.9583 0.9583 72
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+ wildebeest 0.9577 0.9444 0.9510 72
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+ wombat 0.8947 0.9577 0.9252 71
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+ yak 1.0000 0.9437 0.9710 71
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+ zebra 0.9595 1.0000 0.9793 71
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+
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+ accuracy 0.9572 3204
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+ macro avg 0.9587 0.9573 0.9572 3204
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+ weighted avg 0.9586 0.9572 0.9572 3204
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+ ```