initializing repo

test.py

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+import logging
+from ultralytics import YOLO
+
+# Suppress Ultralytics logging
+logging.getLogger('ultralytics').setLevel(logging.WARNING)
+
+# Define models and the test image
+models = {
+    "yolov8n": "yolov8n.pt",  # Pretrained model
+    "fine_tuned": "yolov8n_rubberducks.pt"   # Fine-tuned model
+}
+image_path = "test_image.jpg"
+
+# Initialize a dictionary to store results
+performance = {}
+
+# Run inference for each model
+for model_name, model_path in models.items():
+    # Load the model
+    model = YOLO(model_path)
+    
+    # Run inference on the test image
+    results = model(image_path)
+    first_result = results[0]  # Extract the first result
+    
+    # Count the number of detections (boxes)
+    num_detections = len(first_result.boxes) if hasattr(first_result, 'boxes') and first_result.boxes is not None else 0
+    
+    # Calculate total confidence score of detections
+    if num_detections > 0:
+        total_confidence = sum(float(box.conf) for box in first_result.boxes)  # Convert tensor to float
+    else:
+        total_confidence = 0.0  # No detections
+    
+    # Store performance data
+    performance[model_name] = {
+        "detections": num_detections,
+        "confidence": total_confidence
+    }
+
+# Extract results for comparison
+yolo_detections = performance['yolov8n']['detections']
+yolo_confidence = performance['yolov8n']['confidence']
+fine_tuned_detections = performance['fine_tuned']['detections']
+fine_tuned_confidence = performance['fine_tuned']['confidence']
+
+# Calculate the difference
+diff_detections = fine_tuned_detections - yolo_detections
+diff_confidence = fine_tuned_confidence - yolo_confidence
+detection_diff_word = "more" if diff_detections > 0 else "less"
+confidence_diff_word = "more" if diff_confidence > 0 else "less"
+
+# Print streamlined results
+print()
+print(f"    YOLOv8n detected {yolo_detections} ducks with a total confidence of {yolo_confidence:.2f}")
+print(f"    The fine-tuned model detected {fine_tuned_detections} ducks with a total confidence of {fine_tuned_confidence:.2f}")
+print(f"    The fine-tuned model detects ducks with {abs(diff_confidence * 100):.0f}% {confidence_diff_word} confidence.")
+print()

yolov8n_rubberducks.pt

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