add ultralytics model card

README.md

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+ 
+---
+tags:
+- ultralyticsplus
+- yolov8
+- ultralytics
+- yolo
+- vision
+- object-detection
+- pytorch
+
+library_name: ultralytics
+library_version: 8.0.43
+inference: false
+
+model-index:
+- name: foduucom/plant-leaf-detection-and-classification
+  results:
+  - task:
+      type: object-detection
+
+    metrics:
+      - type: precision  # since [email protected] is not available on hf.co/metrics
+        value: 0.58305  # min: 0.0 - max: 1.0
+        name: [email protected](box)
+---
+
+<div align="center">
+  <img width="640" alt="foduucom/plant-leaf-detection-and-classification" src="https://huggingface.co/foduucom/plant-leaf-detection-and-classification/resolve/main/thumbnail.jpg">
+</div>
+
+### Supported Labels
+
+```
+['ginger', 'banana', 'tobacco', 'ornamaental', 'rose', 'soyabean', 'papaya', 'garlic', 'raspberry', 'mango', 'cotton', 'corn', 'pomgernate', 'strawberry', 'Blueberry', 'brinjal', 'potato', 'wheat', 'olive', 'rice', 'lemon', 'cabbage', 'gauava', 'chilli', 'capcicum', 'sunflower', 'cherry', 'cassava', 'apple', 'tea', 'sugarcane', 'groundnut', 'weed', 'peach', 'coffee', 'cauliflower', 'tomato', 'onion', 'gram', 'chiku', 'jamun', 'castor', 'pea', 'cucumber', 'grape', 'cardamom']
+```
+
+### 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, render_result
+
+# load model
+model = YOLO('foduucom/plant-leaf-detection-and-classification')
+
+# set model parameters
+model.overrides['conf'] = 0.25  # NMS confidence threshold
+model.overrides['iou'] = 0.45  # NMS IoU threshold
+model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+model.overrides['max_det'] = 1000  # maximum number of detections per image
+
+# 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].boxes)
+render = render_result(model=model, image=image, result=results[0])
+render.show()
+```
+