Upload YOLOv11 segmentation model

README.md

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+---
+library_name: ultralytics
+tags:
+- yolov11
+- object-detection
+- instance-segmentation
+- computer-vision
+- deep-learning
+license: agpl-3.0
+---
+
+# YOLOv11 Segmentation Model
+
+This is a custom trained YOLOv11 segmentation model.
+
+## Model Details
+
+- **Model Type**: YOLOv11 Instance Segmentation
+- **Framework**: Ultralytics
+- **Task**: Instance Segmentation
+
+## Usage
+
+### Using Ultralytics
+
+```python
+from ultralytics import YOLO
+
+# Load model
+model = YOLO('model.pt')
+
+# Run inference
+results = model('image.jpg')
+
+# Process results
+for result in results:
+    masks = result.masks  # Segmentation masks
+    boxes = result.boxes  # Bounding boxes
+    
+    # Visualize
+    result.show()
+```
+
+### Using Hugging Face Inference API
+
+```python
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/Sunix2026/Port-model"
+headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
+
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL, headers=headers, data=data)
+    return response.json()
+
+output = query("image.jpg")
+```
+
+## Training Details
+
+Add your training details here:
+- Dataset used
+- Training epochs
+- Hyperparameters
+- Performance metrics
+
+## License
+
+AGPL-3.0

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f685fe1282b37eed408aa87310738d5d33be6247210bdd3fe3252756c4e27850
+size 124882977

requirements.txt

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+ultralytics>=8.0.0
+torch>=2.0.0
+torchvision>=0.15.0
+opencv-python>=4.7.0
+pillow>=9.0.0
+numpy>=1.23.0