How to use Yolov8

A new model for object detection.
Easier to use.

GitHub - ultralytics/ultralytics: YOLOv8 🚀 in PyTorch > ONNX > CoreML > TFLite

Usage

Install

pip install ultralytics

Predict

Object Detection

yolo task=detect mode=predict model=yolov8s.pt source="https://ultralytics.com/images/bus.jpg"

Semantic Segmentation

yolo task=segment mode=predict　model=yolov8s-seg.pt source="image.jpg"

Classification

yolo task=classify mode=predict model=yolov8s-cls.pt source="bird.jpg"

only this.

source can be an image URL, a local path, or a video.

model can be selected from the following.

from official repo.

Larger models are slower but more accurate.

yolov8s

yolov8x

training

yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640

If you replace the contents of coco128.yaml with your own data, you can learn with your own data.

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco128  # dataset root dir
train: images/train2017  # train images (relative to 'path') 128 images
val: images/train2017  # val images (relative to 'path') 128 images
test:  # test images (optional)

# Classes
names:
  0: person
  1: bicycle
  2: car
  3: motorcycle
  4: airplane
  5: bus
  6: train
  7: truck
  8: boat
  9: traffic light
  10: fire hydrant
  11: stop sign
  12: parking meter
  13: bench
  14: bird
  15: cat
  16: dog
  17: horse
  18: sheep
  19: cow
  20: elephant
  21: bear
  22: zebra
  23: giraffe
  24: backpack
  25: umbrella
  26: handbag
  27: tie
  28: suitcase
  29: frisbee
  30: skis
  31: snowboard
  32: sports ball
  33: kite
  34: baseball bat
  35: baseball glove
  36: skateboard
  37: surfboard
  38: tennis racket
  39: bottle
  40: wine glass
  41: cup
  42: fork
  43: knife
  44: spoon
  45: bowl
  46: banana
  47: apple
  48: sandwich
  49: orange
  50: broccoli
  51: carrot
  52: hot dog
  53: pizza
  54: donut
  55: cake
  56: chair
  57: couch
  58: potted plant
  59: bed
  60: dining table
  61: toilet
  62: tv
  63: laptop
  64: mouse
  65: remote
  66: keyboard
  67: cell phone
  68: microwave
  69: oven
  70: toaster
  71: sink
  72: refrigerator
  73: book
  74: clock
  75: vase
  76: scissors
  77: teddy bear
  78: hair drier
  79: toothbrush


# Download script/URL (optional)
download: https://ultralytics.com/assets/coco128.zip

Conversion to various formats

yolo mode=export model=yolov8s.pt format=onnx

From official repo.

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