Delete README.md

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()
-```
-