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add ultralytics model card

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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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+
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+ library_name: ultralytics
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+ library_version: 8.0.43
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+ inference: false
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+
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+ model-index:
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+ - name: uisikdag/weed_yolov8_balanced
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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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+ metrics:
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+ - type: accuracy
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+ value: 0.9 # min: 0.0 - max: 1.0
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+ name: top1 accuracy
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+ - type: accuracy
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+ value: 1.0 # 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="uisikdag/weed_yolov8_balanced" src="https://huggingface.co/uisikdag/weed_yolov8_balanced/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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+ ['Black-grass', 'Charlock', 'Cleavers', 'Common Chickweed', 'Common wheat', 'Fat Hen', 'Loose Silky-bent', 'Maize', 'Scentless Mayweed', 'Shepherds Purse', 'Small-flowered Cranesbill', 'Sugar beet']
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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==0.0.28 ultralytics==8.0.43
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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('uisikdag/weed_yolov8_balanced')
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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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+