数据集:
keremberke/indoor-scene-classification
任务:
图像分类['meeting_room', 'cloister', 'stairscase', 'restaurant', 'hairsalon', 'children_room', 'dining_room', 'lobby', 'museum', 'laundromat', 'computerroom', 'grocerystore', 'hospitalroom', 'buffet', 'office', 'warehouse', 'garage', 'bookstore', 'florist', 'locker_room', 'inside_bus', 'subway', 'fastfood_restaurant', 'auditorium', 'studiomusic', 'airport_inside', 'pantry', 'restaurant_kitchen', 'casino', 'movietheater', 'kitchen', 'waitingroom', 'artstudio', 'toystore', 'kindergarden', 'trainstation', 'bedroom', 'mall', 'corridor', 'bar', 'classroom', 'shoeshop', 'dentaloffice', 'videostore', 'laboratorywet', 'tv_studio', 'church_inside', 'operating_room', 'jewelleryshop', 'bathroom', 'clothingstore', 'closet', 'winecellar', 'livingroom', 'nursery', 'gameroom', 'inside_subway', 'deli', 'bakery', 'library', 'prisoncell', 'gym', 'concert_hall', 'greenhouse', 'elevator', 'poolinside', 'bowling']
{'train': 10885, 'test': 1558, 'valid': 3128}
pip install datasets
from datasets import load_dataset ds = load_dataset("keremberke/indoor-scene-classification", name="full") example = ds['train'][0]
https://universe.roboflow.com/popular-benchmarks/mit-indoor-scene-recognition/dataset/5
MIT
此数据集于2022年10月24日上午4:09(GMT)通过roboflow.com导出
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共包含15571个图像。室内场景以文件夹格式进行注释。
对每个图像应用了以下预处理方法:
未应用任何图像增强技术。