README.md

metadata

tags:
  - ultralyticsplus
  - yolov8
  - ultralytics
  - yolo
  - vision
  - image-classification
  - pytorch
  - awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.20
inference: false
datasets:
  - keremberke/indoor-scene-classification
model-index:
  - name: keremberke/yolov8n-scene-classification
    results:
      - task:
          type: image-classification
        dataset:
          type: keremberke/indoor-scene-classification
          name: indoor-scene-classification
          split: validation
        metrics:
          - type: accuracy
            value: 0.01605
            name: top1 accuracy
          - type: accuracy
            value: 0.08793
            name: top5 accuracy

Supported Labels

['airport_inside', 'artstudio', 'auditorium', 'bakery', 'bookstore', 'bowling', 'buffet', 'casino', 'children_room', 'church_inside', 'classroom', 'cloister', 'closet', 'clothingstore', 'computerroom', 'concert_hall', 'corridor', 'deli', 'dentaloffice', 'dining_room', 'elevator', 'fastfood_restaurant', 'florist', 'gameroom', 'garage', 'greenhouse', 'grocerystore', 'gym', 'hairsalon', 'hospitalroom', 'inside_bus', 'inside_subway', 'jewelleryshop', 'kindergarden', 'kitchen', 'laboratorywet', 'laundromat', 'library', 'livingroom', 'lobby', 'locker_room', 'mall', 'meeting_room', 'movietheater', 'museum', 'nursery', 'office', 'operating_room', 'pantry', 'poolinside', 'prisoncell', 'restaurant', 'restaurant_kitchen', 'shoeshop', 'stairscase', 'studiomusic', 'subway', 'toystore', 'trainstation', 'tv_studio', 'videostore', 'waitingroom', 'warehouse', 'winecellar']

How to use

• Install ultralyticsplus:

pip install ultralyticsplus==0.0.21

• Load model and perform prediction:

from ultralyticsplus import YOLO, postprocess_classify_output

# load model
model = YOLO('keremberke/yolov8n-scene-classification')

# set model parameters
model.overrides['conf'] = 0.25  # model confidence threshold

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model.predict(image)

# observe results
print(results[0].probs) # [0.1, 0.2, 0.3, 0.4]
processed_result = postprocess_classify_output(model, result=results[0])
print(processed_result) # {"cat": 0.4, "dog": 0.6}