readme:  
  ---
  tags:
  - ultralyticsplus
  - yolov8
  - ultralytics
  - yolo
  - vision
  - image-classification
  - pytorch
  
  library_name: ultralytics
  library_version: 8.0.21
  inference: false
  
  model-index:
  - name: uisikdag/indoorsceneclas_v8
    results:
    - task:
        type: image-classification
  
      metrics:
        - type: accuracy
          value: 0.02246  # min: 0.0 - max: 1.0
          name: top1 accuracy
        - type: accuracy
          value: 0.08986  # min: 0.0 - max: 1.0
          name: top5 accuracy
  ---
  
  <div align="center">
    <img width="640" alt="uisikdag/indoorsceneclas_v8" src="https://huggingface.co/uisikdag/indoorsceneclas_v8/resolve/main/thumbnail.jpg">
    Dataset:<br> <a href="https://universe.roboflow.com/popular-benchmarks/mit-indoor-scene-recognition/dataset/5">Link</a>
  </div>
  
  ### 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](https://github.com/fcakyon/ultralyticsplus):
  
  ```bash
  pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
  ```
  
  - Load model and perform prediction:
  
  ```python
  from ultralyticsplus import YOLO, postprocess_classify_output
  
  # load model
  model = YOLO('uisikdag/indoorsceneclas_v8')
  
  # 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}
  ```
  
  