Create README.md

README.md

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+---
+language:
+- en
+library_name: ultralytics
+pipeline_tag: object-detection
+tags:
+- yolo
+- object-detect
+- yolo11
+- yolov11
+---
+
+# Number and Operator Detection Based on YOLO11x
+
+This repository contains a PyTorch-exported model for detecting sidewalk, car, sign and so on.. using the YOLO11s architecture. The model has been trained to recognize these symbols in images and return their locations and classifications.
+
+## Model Description
+
+The YOLO11s model is optimized for detecting the following:
+
+```text
+#class
+Braille_block
+car
+downstairs
+driveway
+sidewalk
+sign_green
+sign_red
+upstairs
+```
+## How to Use
+
+To use this model in your project, follow the steps below:
+
+### 1. Installation
+
+Ensure you have the `ultralytics` library installed, which is used for YOLO models:
+
+```bash
+pip install ultralytics
+```
+
+### 2. Load the Model
+
+You can load the model and perform detection on an image as follows:
+```python
+from ultralytics import YOLO
+
+# Load the model
+model = YOLO("./yolo11m-sidewalk.pt")
+
+# Perform detection on an image
+results = model("image.png")
+
+# Display or process the results
+results.show()  # This will display the image with detected objects
+```
+
+### 3. Model Inference
+The results object contains bounding boxes, labels (e.g., numbers or operators), and confidence scores for each detected object.
+
+Access them like this:
+
+```python
+for result in results:
+    print(result.boxes)   # Bounding boxes
+    print(result.names)   # Detected classes
+    print(result.scores)  # Confidence scores
+```
+
+![](result.png)
+
+#yolo11