add ultralytics model card

README.md

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+ 
+---
+tags:
+- ultralyticsplus
+- yolov8
+- ultralytics
+- yolo
+- vision
+- image-classification
+- pytorch
+
+library_name: ultralytics
+library_version: 8.0.43
+inference: false
+
+model-index:
+- name: uisikdag/weed_yolov8_balanced
+  results:
+  - task:
+      type: image-classification
+
+    metrics:
+      - type: accuracy
+        value: 0.9  # min: 0.0 - max: 1.0
+        name: top1 accuracy
+      - type: accuracy
+        value: 1.0  # min: 0.0 - max: 1.0
+        name: top5 accuracy
+---
+
+<div align="center">
+  <img width="640" alt="uisikdag/weed_yolov8_balanced" src="https://huggingface.co/uisikdag/weed_yolov8_balanced/resolve/main/thumbnail.jpg">
+</div>
+
+### Supported Labels
+
+```
+['Black-grass', 'Charlock', 'Cleavers', 'Common Chickweed', 'Common wheat', 'Fat Hen', 'Loose Silky-bent', 'Maize', 'Scentless Mayweed', 'Shepherds Purse', 'Small-flowered Cranesbill', 'Sugar beet']
+```
+
+### How to use
+
+- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
+
+```bash
+pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
+```
+
+- Load model and perform prediction:
+
+```python
+from ultralyticsplus import YOLO, postprocess_classify_output
+
+# load model
+model = YOLO('uisikdag/weed_yolov8_balanced')
+
+# 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}
+```
+