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 greed/red/yellow ball 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:
+
+- greed/red/yellow ball
+```text
+#class
+greed ball
+red ball
+yellow ball
+```
+## 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("./balldetect-11s.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