/yolov8nd

YOLOv8-ND: New Detection Algorithm for Fast and Lightweight Object Detection

Primary LanguagePythonGNU Affero General Public License v3.0AGPL-3.0

YOLOv8-ND

YOLOv8-ND: New Detection Algorithm for Fast and Lightweight Object Detection

How to use (Examples)

  • Train: yolov8nd detect train model=yolov8ndn.pt data=coco128.yaml epochs=50
  • Validation: yolov8nd detect val model=yolov8ndn.pt data=coco128.yaml
  • Predict: yolov8nd detect predict model=yolov8ndn.pt source=https://ultralytics.com/images/bus.jpg

Experimental Environment

  • CPU: i5-13500 (E-Core: 2.0GHz, P-Core: 3.0GHz)
  • Memory: DDR4 64GB (3200MHz, 128bit width)
  • GPU: RTX 4070 (max 2.4GHz, 125W)
  • OS: Ubuntu 22.04 LTS

Performance and Comparison

Models Size mAP50-95
(%)
Params
(M)
GFLOPs Speed
GPU(ms)
Speed
CPU(ms)
YOLOv8ndn 640 36.4 3.2 7.6 4.1 36.9
YOLOv8nds 640 44.0 12.3 27.1 4.2 88.2
YOLOv8ndm 640 48.9 26.9 74.9 6.1 205.5
YOLOv8ndl 640 51.1 43.5 157.1 9.6 390.5
YOLOv8ndn-lite 640 34.9 2.4 5.9 3.6 30.9
YOLOv8nds-lite 640 43.2 9.2 21.1 3.7 73.7
YOLOv8ndm-lite 640 48.5 22.2 63.7 5.3 181.4
YOLOv8ndl-lite 640 50.4 38.2 139.7 8.6 352.4
YOLOv8ndn-aux 640 37.5 3.7 8.7 4.4 40.7
YOLOv8nds-aux 640 45.2 14.4 31.3 4.5 99.5
YOLOv8ndm-aux 640 49.4 33.6 91.7 7.2 245.1
YOLOv8ndl-aux 640 51.0 56.3 198.0 11.8 485.0
YOLOv8n 640 37.5 3.2 8.9 4.4 40.9
YOLOv8s 640 44.7 11.2 28.6 4.5 91.5
YOLOv8m 640 50.1 25.9 78.9 6.5 215.1
YOLOv8l 640 52.9 43.7 165.2 10.1 406.5

Train parameters: batch=16 epochs=500 optimizer=SGD lr0=0.01 momentum=0.937 data=coco.yaml

Validation parameters: data=coco.yaml batch=1 device=0|cpu

Acknowlegement

Template from https://github.com/ultralytics/ultralytics (version: 8.2.60)