I remembered I had a bunch of Magic: The Gathering cards in a forgotten binder, and thought about setting myself the following challenge to put them to good use: Can I train a machine learning model that correctly identifies Magic: The Gathering cards? I went through the cards in the binder thinking about the scenario I wanted to explore, and I decided to go with image classification to identify Lands
I was really impressed to see that they made a new picture for it instead (compared to Shooting Quasar Dragon)
View as Gallery View as List GH Ghost Rare UR Ultra Rare SR Super Rare SR Super Rare SR Super Rare SR Super Rare UR Ultra Rare R Rare C Common C Common C Common R Rare C Common C Common C Common C Common UR Ultra Rare UR Ultra Rare SR Super Rare SR Super Rare UR Ultra Rare R Rare R Rare C Common C Common C Common C Common C Common C Common C Common C Common SR Super Rare R Rare UR Ultra Rare UR Ultra Rare UR Ultra Rare R Rare R Rare C Common C Common C Common R Rare C Common C Common C Common C Common C Common R Rare C Common R Rare C Common C Common C Common SR Super Rare C Common C Common UR Ultra Rare Number 101: Silent Honor ARK X WATER Rank 4 [ AquaXyzEffect ] ATK 2100 DEF 1000 2 Level 4 monsters You can detach 2 materials from this card, then target 1 Special Summoned monster your opponent controls in face-up Attack Position
The attack isn't affected by any effects on their active Pokemon
As target removal, we have Feed the Swarm and Chaos Warp to deal with enchantments and similar, Vandalblast and Rakdos Charm to deal with artifacts or answer with the other options in this card, and Bitter Triumph to deal with creatures and planeswalkers
Number 101: Silent Honor ARK provides an amazing way to get rid of your opponents monsters, while also ensuring they dont bring them back any time soon: Simply attach that monster as an XYZ material to this card
