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add ultralytics model card

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  1. README.md +79 -0
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+
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+ ---
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+ tags:
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+ - ultralyticsplus
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+ - yolov8
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+ - ultralytics
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+ - yolo
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+ - vision
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+ - image-classification
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+ - pytorch
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+ - awesome-yolov8-models
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+ library_name: ultralytics
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+ library_version: 8.0.20
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+ inference: false
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+
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+ datasets:
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+ - keremberke/indoor-scene-classification
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+
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+ model-index:
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+ - name: keremberke/yolov8n-scene-classification
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+ results:
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+ - task:
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+ type: image-classification
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+
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+ dataset:
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+ type: keremberke/indoor-scene-classification
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+ name: indoor-scene-classification
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+ split: validation
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+
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+ metrics:
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+ - type: accuracy
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+ value: 0.01733 # min: 0.0 - max: 1.0
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+ name: top1 accuracy
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+ - type: accuracy
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+ value: 0.0751 # min: 0.0 - max: 1.0
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+ name: top5 accuracy
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+ ---
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+
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+ <div align="center">
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+ <img width="640" alt="keremberke/yolov8n-scene-classification" src="https://huggingface.co/keremberke/yolov8n-scene-classification/resolve/main/thumbnail.jpg">
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+ </div>
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+
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+ ### Supported Labels
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+
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+ ```
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+ ['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']
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+ ```
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+
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+ ### How to use
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+
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+ - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
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+
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+ ```bash
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+ pip install ultralyticsplus==0.0.21
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+ ```
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+
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+ - Load model and perform prediction:
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+
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+ ```python
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+ from ultralyticsplus import YOLO, postprocess_classify_output
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+
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+ # load model
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+ model = YOLO('keremberke/yolov8n-scene-classification')
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+
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+ # set model parameters
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+ model.overrides['conf'] = 0.25 # model confidence threshold
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+
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+ # set image
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+ image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
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+
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+ # perform inference
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+ results = model.predict(image)
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+
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+ # observe results
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+ print(results[0].probs) # [0.1, 0.2, 0.3, 0.4]
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+ processed_result = postprocess_classify_output(model, result=results[0])
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+ print(processed_result) # {"cat": 0.4, "dog": 0.6}
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+ ```
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+