Update README.md

README.md

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 ---
 license: cc-by-sa-4.0
+datasets:
+- KaraAgroAI/CADI-AI
+language:
+- en
+metrics:
+- mape
+pipeline_tag: object-detection
+tags:
+- object detection
+- vision
 ---
+
+
+## Cashew Disease Identification with AI (CADI-AI) Model
+
+### Model Description
+
+Object detection model trained using YOLO v5x. The model was pre-trained on the Cashew Disease Identification with AI (CADI-AI) train set (3788 images) at a resolution of 640x640 pixels.
+CADI-AI dataset is available in hugging face dataset hub
+
+## Intended uses & limitations
+
+You can use the raw model for object detection on cashew images.
+
+### How to use
+
+- Load model and perform prediction:
+
+```python
+import torch
+
+# load model
+model = torch.hub.load('ultralytics/yolov5', 'KaraAgroAI/CADI-AI')
+
+# Images
+img = ['/path/to/CADI-AI-image.jpg']# batch of images
+  
+# set model parameters
+model.conf = 0.20  # NMS confidence threshold
+
+# perform inference
+results = model(img, size=640)
+
+# Results
+results.print()
+
+results.xyxy[0]  # img1 predictions (tensor)
+results.pandas().xyxy[0]  # img1 predictions (pandas)
+
+# parse results
+predictions = results.pred[0]
+boxes = predictions[:, :4] # x1, y1, x2, y2
+scores = predictions[:, 4]
+categories = predictions[:, 5]
+
+# show detection bounding boxes on image
+results.show()
+
+# save results into "results/" folder
+results.save(save_dir='results/')
+```
+
+- Finetune the model on your custom dataset:
+
+```bash
+yolov5 train --data data.yaml --img 640 --batch 16 --weights KaraAgroAI/CADI-AI --epochs 10
+```