Object Detection - YOLO

This case study demonstrates training YOLOv8-Nano for real-time object detection. YOLO (You Only Look Once) is a state-of-the-art model that detects multiple objects in images with bounding boxes and class labels in a single forward pass, making it ideal for real-time applications.

Dataset: COCO (Common Objects in Context)

• Source: HuggingFace (detection-datasets/coco)
• Type: Object detection
• Size: 118,287 training images
• Classes: 80 object categories
• Annotations: Bounding boxes with class labels
• Format: Images with JSON annotations

Model Configuration

{
  "model": "yolov8_nano",
  "category": "computer_vision",
  "subcategory": "object-detection",
  "model_config": {
    "pretrained": true,
    "conf_threshold": 0.25,
    "iou_threshold": 0.45,
    "batch_size": 16,
    "epochs": 100,
    "learning_rate": 0.01,
    "image_size": [640, 640]
  }
}

Training Results

mAP Performance

Mean Average Precision at different IoU thresholds:

Detection by Object Size

Performance varies with object size:

Top Performing Classes

Best detected object categories:

Inference Speed vs Accuracy

YOLOv8 model variants comparison:

Training Metrics

Loss components over training epochs:

Common Use Cases

• Autonomous Vehicles: Detect pedestrians, vehicles, traffic signs
• Surveillance: Monitor people and objects in security footage
• Retail Analytics: Track customer behavior, product placement
• Sports Analytics: Track players and ball position
• Industrial Inspection: Detect defects or parts on assembly lines
• Wildlife Monitoring: Count and track animals in camera traps
• Medical Imaging: Detect tumors or abnormalities in scans

Key Settings

Essential Parameters

• conf_threshold: Minimum confidence for detections (0.25 default)
• iou_threshold: IoU threshold for NMS (0.45 typical)
• image_size: Input resolution (640x640 standard)
• batch_size: Images per training iteration
• epochs: Training iterations (100-300 typical)

Data Augmentation

• mosaic: Combine 4 images into one (improves small object detection)
• mixup: Blend two images (improves generalization)
• hsv: HSV color space augmentation
• flip: Horizontal flipping
• scale: Random scaling (0.5-1.5x)
• translate: Random translation

Advanced Configuration

• anchor_optimization: Auto-tune anchor boxes
• multi_scale: Train on multiple image sizes
• label_smoothing: Soften hard labels (0.0-0.1)
• warmup_epochs: Learning rate warmup period
• close_mosaic: Disable mosaic in final epochs

Performance Metrics

• mAP@0.5:0.95: 56.2% (COCO standard metric)
• mAP@0.5: 83.5% (IoU threshold 0.5)
• Precision: 81.3%
• Recall: 76.8%
• Inference Speed: 142 FPS (NVIDIA RTX 3080)
• Model Size: 6.2 MB (Nano variant)
• Parameters: 3.2 million

Tips for Success

1. Image Quality: Use high-resolution images with clear objects
2. Balanced Data: Ensure all classes have sufficient examples
3. Proper Annotations: Verify bounding boxes are accurate
4. Augmentation: Essential for small datasets, disable in final epochs
5. Multi-Scale Training: Improves detection across object sizes
6. NMS Tuning: Adjust IoU threshold for overlapping objects
7. Anchor Boxes: Let model auto-optimize for your dataset

Example Scenarios

Scenario 1: Street Scene

• Input: Urban street image
• Detections: 3 persons, 5 cars, 1 bicycle, 2 traffic lights
• Confidence: 85-95% for all detections
• Processing Time: 7ms (142 FPS)

Scenario 2: Indoor Room

• Input: Living room photo
• Detections: 1 person, 1 couch, 1 TV, 2 chairs, 1 laptop, 1 potted plant
• Confidence: 80-92%
• Processing Time: 7ms

Scenario 3: Retail Store

• Input: Store aisle surveillance
• Detections: 4 persons, 12 bottles, 3 handbags
• Use Case: Customer analytics, inventory tracking

Troubleshooting

Problem: Missing small objects

• Solution: Increase image resolution, use mosaic augmentation, train longer

Problem: Many false positives

• Solution: Increase conf_threshold, add more negative examples

Problem: Poor localization (boxes not tight)

• Solution: Verify annotation quality, increase box_loss weight

Problem: Class confusion (misclassifying similar objects)

• Solution: Add more training data for confused classes, increase class_loss weight

Problem: Slow inference speed

• Solution: Use smaller model variant (Nano), reduce image size, use TensorRT

Model Architecture Highlights

YOLOv8-Nano consists of:

• Backbone: C2f modules for feature extraction
• Neck: PAN (Path Aggregation Network) for multi-scale features
• Head: Decoupled heads for classification and localization
• Anchor-free: Direct prediction without anchor boxes
• Task-aligned: Unified loss for classification and localization

Next Steps

After training your YOLOv8 model, you can:

• Deploy as REST API or edge device
• Export to ONNX, TensorRT, CoreML for production
• Implement object tracking (ByteTrack, BoT-SORT)
• Add custom classes with transfer learning
• Create ensemble models for higher accuracy
• Integrate with video processing pipelines
• Optimize for specific hardware (Jetson, Coral, iPhone)