steps = 8
n_runs = 1
img_path = 'datasets/CIFAR100/images/'
data_root = 'exp/linear_probe/CIFAR100'
img_split_path = 'datasets/CIFAR100/splits'
num_cls = 100
unfreeze_clip = False
paper = True
cls_names = ['apple', 'aquarium fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak tree', 'orange', 'orchid', 'otter', 'palm tree', 'pear', 'pickup truck', 'pine tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow tree', 'wolf', 'woman', 'worm']
img_ext=''
clip_model='ViT-L/14'

lr=1e-3
bs=128
n_shots = 8
dataset = 'CIFAR100'