birds.yaml

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# COCO 2017 dataset http://cocodataset.org
# Example usage: python train.py --data coco.yaml
# parent
# ├── yolov5
# └── datasets
#     └── coco  ← downloads here


# 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, ..]
path: C://Users/fm873/Documents/Classes/CSMachineLearning/ObjectDetectionProject/code/yolov5/myData/birdImages/  # dataset root dir
train: train.txt  # train images (relative to 'path') 118287 images
val: train.txt  # train images (relative to 'path') 5000 images
test: test.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794

# Classes
nc: 34  # number of classes
names: [001.Black_footed_Albatross,
002.Laysan_Albatross,
003.Sooty_Albatross,
004.Groove_billed_Ani,
005.Crested_Auklet,
006.Least_Auklet,
007.Parakeet_Auklet,
008.Rhinoceros_Auklet,
009.Brewer_Blackbird,
010.Red_winged_Blackbird,
011.Rusty_Blackbird,
012.Yellow_headed_Blackbird,
013.Bobolink,
014.Indigo_Bunting,
015.Lazuli_Bunting,
016.Painted_Bunting,
017.Cardinal,
018.Spotted_Catbird,
019.Gray_Catbird,
020.Yellow_breasted_Chat,
021.Eastern_Towhee,
022.Chuck_will_Widow,
023.Brandt_Cormorant,
024.Red_faced_Cormorant,
025.Pelagic_Cormorant,
026.Bronzed_Cowbird,
027.Shiny_Cowbird,
028.Brown_Creeper,
029.American_Crow,
030.Fish_Crow,
031.Black_billed_Cuckoo,
032.Mangrove_Cuckoo,
033.Yellow_billed_Cuckoo,
034.Gray_crowned_Rosy_Finch]  # class names