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Blood-Cancer-Dataset-Luke
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license





  
  

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# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University





  
  

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# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels





  
  

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# Example usage: python classify/train.py --data imagenet





  
  

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# parent





  
  

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# ├── yolov5





  
  

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# └── datasets





  
  

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#     └── imagenet100  ← downloads here





  
  

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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]





  
  

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path: ../datasets/imagenet100  # dataset root dir





  
  

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train: train  # train images (relative to 'path') 1281167 images





  
  

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val: val  # val images (relative to 'path') 50000 images





  
  

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test:  # test images (optional)





  
  

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# Classes





  
  

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names:





  
  

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  0: tench





  
  

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  1: goldfish





  
  

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  2: great white shark





  
  

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  3: tiger shark





  
  

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  4: hammerhead shark





  
  

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  5: electric ray





  
  

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  6: stingray





  
  

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  7: cock





  
  

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  8: hen





  
  

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  9: ostrich





  
  

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  10: brambling





  
  

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  11: goldfinch





  
  

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  12: house finch





  
  

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  13: junco





  
  

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  14: indigo bunting





  
  

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  15: American robin





  
  

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  16: bulbul





  
  

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  17: jay





  
  

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  18: magpie





  
  

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  19: chickadee





  
  

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  20: American dipper





  
  

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  21: kite





  
  

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  22: bald eagle





  
  

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  23: vulture





  
  

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  24: great grey owl





  
  

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  25: fire salamander





  
  

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  26: smooth newt





  
  

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  27: newt





  
  

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  28: spotted salamander





  
  

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  29: axolotl





  
  

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  30: American bullfrog





  
  

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  31: tree frog





  
  

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  32: tailed frog





  
  

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  33: loggerhead sea turtle





  
  

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  34: leatherback sea turtle





  
  

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  35: mud turtle





  
  

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  36: terrapin





  
  

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  37: box turtle





  
  

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  38: banded gecko





  
  

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  39: green iguana





  
  

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  40: Carolina anole





  
  

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  41: desert grassland whiptail lizard





  
  

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  42: agama





  
  

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  43: frilled-necked lizard





  
  

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  44: alligator lizard





  
  

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  45: Gila monster





  
  

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  46: European green lizard





  
  

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  47: chameleon





  
  

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  48: Komodo dragon





  
  

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  49: Nile crocodile





  
  

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  50: American alligator





  
  

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  51: triceratops





  
  

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  52: worm snake





  
  

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  53: ring-necked snake





  
  

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  54: eastern hog-nosed snake





  
  

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  55: smooth green snake





  
  

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  56: kingsnake





  
  

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  57: garter snake





  
  

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  58: water snake





  
  

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  59: vine snake





  
  

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  60: night snake





  
  

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  61: boa constrictor





  
  

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  62: African rock python





  
  

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  63: Indian cobra





  
  

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  64: green mamba





  
  

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  65: sea snake





  
  

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  66: Saharan horned viper





  
  

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  67: eastern diamondback rattlesnake





  
  

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  68: sidewinder





  
  

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  69: trilobite





  
  

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  70: harvestman





  
  

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  71: scorpion





  
  

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  72: yellow garden spider





  
  

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  73: barn spider





  
  

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  74: European garden spider





  
  

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  75: southern black widow





  
  

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  76: tarantula





  
  

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  77: wolf spider





  
  

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  78: tick





  
  

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  79: centipede





  
  

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  80: black grouse





  
  

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  81: ptarmigan





  
  

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  82: ruffed grouse





  
  

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  83: prairie grouse





  
  

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  84: peacock





  
  

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  85: quail





  
  

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  86: partridge





  
  

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  87: grey parrot





  
  

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  88: macaw





  
  

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  89: sulphur-crested cockatoo





  
  

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  90: lorikeet





  
  

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  91: coucal





  
  

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  92: bee eater





  
  

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  93: hornbill





  
  

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  94: hummingbird





  
  

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  95: jacamar





  
  

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  96: toucan





  
  

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  97: duck





  
  

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  98: red-breasted merganser





  
  

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  99: goose





  
  

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# Download script/URL (optional)





  
  

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download: data/scripts/get_imagenet100.sh