--- 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 # min: 0.0 - max: 1.0 name: top1 accuracy - type: accuracy value: 0.08793 # min: 0.0 - max: 1.0 name: top5 accuracy ---
keremberke/yolov8n-scene-classification
### 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==0.0.21 ``` - Load model and perform prediction: ```python 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} ```