Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+base_model:
+- Ultralytics/YOLOv8
+pipeline_tag: object-detection
+---
+# Overview
+This repository hosts a YOLOv8l model trained on the ArxivFormula (https://github.com/microsoft/ArxivFormula) dataset, which focuses on the detection of mathematical expressions in scientific papers.
+
+# Training Data:
+- Source: ArxivFormula (https://github.com/microsoft/ArxivFormula)
+- Classes: 6 classes (InlineFormula, DisplayedFormulaLine, FormulaNumber, DisplayedFormulaBlock, Table, Figure)
+  Pages: ~600,000 images of document pages
+
+# Model:
+- YOLOv8l (https://github.com/ultralytics/ultralytics)
+- epochs = 100
+- imgsz = 640
+- optimizer = 'AdamW'
+- lr0 = 0.0001
+- augment = True
+
+# Usage
+## Example Code
+
+```
+from ultralytics import YOLO
+import pathlib
+
+# Sample images
+img_list = ['sample1.png', 'sample2.png', 'sample3.png']
+
+# Load the document segmentation model
+model = YOLO('arxivFormula_YOLOv8l.pt')
+
+# Process the images
+results = model(source=img_list, save=True, show_labels=True, show_conf=True, show_boxes=True)
+```