Update README.md

README.md

@@ -11,15 +11,15 @@ metrics:
 pipeline_tag: object-detection
 ---
 
-# Pest Detection Model - YOLOv5
+# Pest Detection Model - YOLO11
 
 ## Introduction
-This model was trained to detect various pests in agricultural settings using YOLOv5. The goal of this model is to assist farmers and agronomists in identifying pests to help in better crop management. The model was trained on a custom dataset and has been optimized for accuracy and efficiency in identifying different types of pests.
+This model was trained to detect various pests in agricultural settings using YOLO11. The goal of this model is to assist farmers and agronomists in identifying pests to help in better crop management. The model was trained on a custom dataset and has been optimized for accuracy and efficiency in identifying different types of pests.
 
 ## Model Details
 - **Model File**: [best.pt](./best.pt)
-- **Framework**: YOLOv5
-- **Dataset**: Custom dataset containing images of agricultural pests.
+- **Framework**: YOLO11
+- **Dataset**: https://www.kaggle.com/datasets/leonidkulyk/ip102-yolov5
 
 ## Training Metrics
 Below are the metrics and results from the training process:
@@ -34,6 +34,10 @@ Below are the metrics and results from the training process:
 
 *Note: Metrics include precision, recall, mAP at 50% (mAP50), and mAP across 50-95% (mAP50-95) confidence.*
 
+## Additional Files
+- [Training Metrics CSV](./results.csv): A detailed CSV file containing training metrics and results.
+
+
 ## Training Graphs
 Below is the graph representing the model's training process, including metrics such as loss, precision, and recall.