1.train.py的使用

python train.py --weights yolov5s.py --data data/coco128.yaml

使用coco128数据集和预训练的yolov5s的模型训练自己的模型

coco128.yaml

path:  D:/yolov5-master/datasets/coco128  # 必须使用绝对路径  # dataset root dir
train: images/train  # train images (relative to 'path') 128 images
val:images/value  # 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

2.detect.py的使用

python detect.py --weights /runs/train/exp/weights/best.pt --data data/coco128.yaml --source data/001.jpg/ data/002.mp4 /try(文件夹，里面既可以是图片，也可以是视频)/ screen(屏幕)/ 0(摄像头)

--conf-thres  置信度（置信度越大，框越少，置信度越小，框越多）

--iou-thres  IOU值

3.pytorch中使用yolo

import torch
# 使用本地的yolov5模型
model=torch.hub.load("./","yolov5s",source="local")
img="./data/001.jpg"
result=model(img)
result.show()

3.数据集的准备