Upload YOLOv11 segmentation model with config

README.md

@@ -6,22 +6,57 @@ tags:
 - instance-segmentation
 - computer-vision
 - deep-learning
+- port-detection
 license: agpl-3.0
 ---
 
-# YOLOv11 Segmentation Model
+# Port Model
 
-This is a custom trained YOLOv11 segmentation model.
+This is a custom trained YOLOv11 segmentation model for port detection.
 
 ## Model Details
 
 - **Model Type**: YOLOv11 Instance Segmentation
-- **Framework**: Ultralytics
+- **Framework**: Ultralytics YOLOv11
 - **Task**: Instance Segmentation
+- **Classes**: 2
+- **Input Size**: 1408x1408
+- **Dataset**: Custom Port Dataset
+
+## Classes
+
+- Class 0: Port-capped
+- Class 1: Port-Empty
+
+## Model Configuration
+
+```json
+{
+  "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
+### Using Ultralytics (Local Inference)
 
 ```python
 from ultralytics import YOLO
@@ -30,13 +65,20 @@ from ultralytics import YOLO
 model = YOLO('model.pt')
 
 # Run inference
-results = model('image.jpg')
+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()
 ```
@@ -45,8 +87,9 @@ for result in results:
 
 ```python
 import requests
+import json
 
-API_URL = "https://api-inference.huggingface.co/models/Sunix2026/Port-model"
+API_URL = "https://router.huggingface.co/models/Sunix2026/Port-model"
 headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
 
 def query(filename):
@@ -55,17 +98,75 @@ def query(filename):
     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']}")
 ```
 
-## Training Details
+## Performance Metrics
+
+| Metric | Value |
+|--------|-------|
+| Confidence Threshold | 0.25 |
+| IoU Threshold | 0.45 |
+| Input Resolution | 1408x1408 |
+
+## Applications
 
-Add your training details here:
-- Dataset used
-- Training epochs
-- Hyperparameters
-- Performance metrics
+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}}
+}
+```

config.json

@@ -0,0 +1,13 @@
+{
+  "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
+}

inference_example.py

@@ -0,0 +1,27 @@
+
+# Sample inference script
+from ultralytics import YOLO
+import json
+
+# Load configuration
+with open('config.json', 'r') as f:
+    config = json.load(f)
+
+# Load model
+model = YOLO('model.pt')
+
+# Run inference with config parameters
+results = model(
+    'your_image.jpg',
+    conf=config['confidence_threshold'],
+    iou=config['iou_threshold'],
+    imgsz=config['input_size']
+)
+
+# Process results
+for result in results:
+    for box in result.boxes:
+        class_id = int(box.cls)
+        class_name = config['id2label'][str(class_id)]
+        confidence = float(box.conf)
+        print(f"Detected: {class_name} ({confidence:.2%})")