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README.md

@@ -1,8 +1,8 @@
-Based on your excellent training results, here's a comprehensive README.md file for your Hugging Face model repository:
-
 ```markdown
 ---
 license: mit
+language:
+- en
 tags:
 - object-detection
 - yolov8
@@ -10,16 +10,18 @@ tags:
 - agriculture
 - computer-vision
 - pytorch
+- ultralytics
 library_name: ultralytics
+pipeline_tag: object-detection
 datasets:
 - qwer0213/PDT_dataset
 metrics:
 - mAP50
 - mAP50-95
-- Precision
-- Recall
+- precision
+- recall
 model-index:
-- name: YOLOv8s Tree Disease Detection
+- name: crop_desease_detection
   results:
   - task:
       type: object-detection
@@ -28,18 +30,22 @@ model-index:
       name: PDT Dataset
       type: qwer0213/PDT_dataset
     metrics:
-    - type: mAP50
+    - type: map
       value: 0.933
       name: mAP50
-    - type: mAP50-95
+    - type: map
       value: 0.659
-      name: mAP50-95  
+      name: mAP50-95
     - type: precision
       value: 0.878
       name: Precision
     - type: recall
       value: 0.863
       name: Recall
+inference: true
+widget:
+  - src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/example_image.jpg
+    example_title: Example Tree Image
 ---
 
 # YOLOv8s Tree Disease Detection Model
@@ -197,90 +203,4 @@ This model is released under the MIT License.
 ## Contact
 
 For questions or collaborations, please reach out through the HuggingFace repository discussions.
-```
-
-You can also create additional files for your repository:
-
-### `config.yaml`:
-```yaml
-# Model Configuration
-
-# Model info
-model_type: yolov8s
-task: object-detection
-architecture: YOLOv8s
-
-# Classes
-nc: 1  # number of classes
-names: ['unhealthy']  # class names
-
-# Input
-imgsz: 640  # inference image size
-
-# Inference settings
-conf: 0.25  # confidence threshold
-iou: 0.45   # IoU threshold for NMS
-max_det: 300  # maximum detections per image
-
-# Device
-device: 0  # cuda device, i.e. 0 or cpu
-```
-
-### `requirements.txt`:
-```
-ultralytics>=8.3.0
-torch>=2.0.0
-opencv-python>=4.8.0
-pillow>=10.0.0
-matplotlib>=3.7.0
-```
-
-### `example.py`:
-```python
-# Example usage of the Tree Disease Detection model
-
-from ultralytics import YOLO
-import cv2
-import matplotlib.pyplot as plt
-
-# Load the model
-model = YOLO('best.pt')
-
-# Example inference on a single image
-def detect_tree_disease(image_path):
-    # Run inference
-    results = model(image_path)
-    
-    # Extract results
-    detections = []
-    for result in results:
-        boxes = result.boxes
-        if boxes is not None:
-            for box in boxes:
-                detection = {
-                    'confidence': float(box.conf[0]),
-                    'bbox': box.xyxy[0].tolist(),
-                    'class': 'unhealthy'
-                }
-                detections.append(detection)
-    
-    # Visualize
-    annotated_img = results[0].plot()
-    plt.figure(figsize=(12, 8))
-    plt.imshow(cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB))
-    plt.axis('off')
-    plt.title(f'Detected {len(detections)} unhealthy tree(s)')
-    plt.show()
-    
-    return detections
-
-# Example usage
-if __name__ == "__main__":
-    # Replace with your image path
-    image_path = 'sample_tree_image.jpg'
-    detections = detect_tree_disease(image_path)
-    
-    print(f"Found {len(detections)} unhealthy trees")
-    for i, det in enumerate(detections):
-        print(f"Detection {i+1}: Confidence={det['confidence']:.2f}")
 ```