{
  "model_type": "yolov11-seg",
  "task": "image-segmentation",
  "framework": "ultralytics",
  "num_classes": 2,
  "id2label": {
    "0": "Port-capped",
    "1": "Port-Empty"
  },
  "input_size": 1408,
  "confidence_threshold": 0.25,
  "iou_threshold": 0.45
}

Training Configuration

Epochs: 100
Batch Size: 16
Optimizer: AdamW
Dataset: Custom Port Dataset

Usage

Using Ultralytics (Local Inference)

python

from ultralytics import YOLO

# Load model
model = YOLO('model.pt')

# Run inference
results = model('image.jpg', conf=0.25, iou=0.45)

# Process results
for result in results:
    masks = result.masks  # Segmentation masks
    boxes = result.boxes  # Bounding boxes
    
    # Get class names
    for box in boxes:
        class_id = int(box.cls)
        class_name = {"0": "Port-capped", "1": "Port-Empty"}[str(class_id)]
        confidence = float(box.conf)
        print(f"Detected: {class_name} ({confidence:.2f})")
    
    # Visualize
    result.show()

Using Hugging Face Inference API

python

import requests
import json

API_URL = "https://router.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()

# Run inference
output = query("image.jpg")
print(json.dumps(output, indent=2))

Using the Python Client

python

from yolov11_hf_inference import YOLOv11HFInference

# Initialize client
client = YOLOv11HFInference(
    model_url="Sunix2026/Port-model",
    access_token="YOUR_HF_TOKEN"
)

# Run inference
result = client.predict_from_path("image.jpg")

if result["success"]:
    predictions = result["predictions"]
    
    # Map class IDs to names
    id2label = {"0": "Port-capped", "1": "Port-Empty"}
    
    for pred in predictions:
        class_name = id2label.get(str(pred.get('label', '')), 'Unknown')
        confidence = pred.get('score', 0)
        print(f"Found: {class_name} ({confidence:.2%})")
else:
    print(f"Error: {result['error']}")

Performance Metrics

Metric	Value
Confidence Threshold	0.25
IoU Threshold	0.45
Input Resolution	1408x1408

Applications

This model can be used for:

Port detection and classification
Automated quality control
Manufacturing inspection
Inventory management

Limitations

Model is trained specifically for port detection
Performance may vary with different lighting conditions
Best results with images similar to training data

License

AGPL-3.0

Citation

If you use this model, please cite:

bibtex

@misc{Port-model,
  author = {Sunix2026},
  title = {Port Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Sunix2026/Port-model}}
}