YOLOv8-Nano

YOLOv8-Nano is the smallest and fastest variant of YOLOv8, designed for real-time object detection on edge devices and mobile platforms. With minimal computational overhead, it achieves impressive detection performance while being deployable on resource-constrained hardware. This model prioritizes speed and efficiency, making it ideal for applications requiring real-time processing.

When to Use YOLOv8-Nano

YOLOv8-Nano is ideal for:

• Real-time applications requiring low latency (<10ms inference)
• Edge devices with limited compute (Raspberry Pi, Jetson, mobile)
• High-throughput systems processing many images per second
• Mobile applications with size and speed constraints
• Projects where inference speed is more critical than maximum accuracy

Strengths

• Extremely fast inference: 2-5ms on modern GPUs, 20-50ms on mobile
• Fast training: Trains 5-10x faster than DETR models
• Lightweight: ~3MB model size deployable anywhere
• Real-time capable: 100+ FPS on desktop GPUs
• Edge-friendly: Runs efficiently on mobile and embedded devices
• Quick convergence: Typically 50-100 epochs sufficient

Weaknesses

• Lower accuracy than DETR models (5-10% lower mAP on complex datasets)
• Anchor-based: Less elegant than DETR's anchor-free approach
• More hyperparameters: Image size, confidence, IoU thresholds to tune
• Less accurate on small objects than Deformable DETR

Parameters

Training Configuration

Training Images: Folder with images Annotations: YOLO or COCO format JSON Batch Size (Default: 16) - Range: 8-64 (much more efficient than DETR) Epochs (Default: 100) - Range: 50-300 Confidence Threshold (Default: 0.25) - Range: 0.0-1.0 for inference filtering IoU Threshold (Default: 0.45) - Range: 0.0-1.0 for NMS Max Detections (Default: 300) - Maximum detections per image Image Size (Default: 640) - Options: 320, 416, 512, 640 pixels

Configuration Tips

Training Settings

• batch_size=16-32 typical (efficient architecture)
• epochs=100 default, can reduce to 50 for fine-tuning
• image_size=640 standard, reduce to 416 for speed, increase to 800 for accuracy
• Much faster training than DETR (hours vs days)

Inference Settings

• confidence_threshold=0.25 default, increase to reduce false positives
• iou_threshold=0.45 for NMS, tune based on overlap tolerance
• max_detections=300 usually sufficient

Dataset Recommendations

• Works well even with small datasets (500+ images)
• Optimal with 2,000+ annotated images
• Less data-hungry than DETR models

Expected Performance

• Speed: 10-20x faster inference than DETR
• Accuracy: 5-10% lower mAP than DETR on complex datasets
• Trade-off: Best speed-accuracy balance for real-time use
• Training: Converges in 50-100 epochs (vs 300-500 for DETR from scratch)

Example Use Cases

Robotics and Autonomous Systems

Real-time object detection for navigation and manipulation. YOLOv8-Nano's speed critical for responsive control systems.

Security Cameras

Process multiple video streams simultaneously. Can handle 10-20 streams on single GPU vs 1-2 with DETR.

Mobile Applications

On-device object detection without cloud dependency. Small size and fast mobile inference enable offline apps.

Industrial Inspection

High-speed quality control on production lines. Inspect hundreds of items per minute with real-time feedback.

Comparison with Alternatives

YOLOv8-Nano vs DETR ResNet-50

Choose YOLOv8-Nano when:

• Need real-time inference (<10ms)
• Edge deployment required
• Processing video streams
• Training time critical
• Model size constraints (<5MB)

Choose DETR ResNet-50 when:

• Accuracy priority over speed
• Offline batch processing
• Research/development setting
• Complex scenes with occlusion
• Elegant architecture preferred

YOLOv8-Nano vs Deformable DETR

Choose YOLOv8-Nano when:

• Real-time requirement (10x faster)
• Edge devices
• Speed critical
• Budget-constrained deployment

Choose Deformable DETR when:

• Maximum accuracy needed
• Small object detection critical
• Inference time acceptable
• Cloud/server deployment

YOLOv8-Nano vs larger YOLO variants

Choose YOLOv8-Nano when:

• Most constrained resources
• Fastest possible inference
• Mobile deployment
• Size <5MB required

Choose larger YOLO (Small/Medium) when**:

• Can afford 2-3x slower inference
• Need 3-5% better accuracy
• Have more powerful hardware
• Not deploying to edge devices