yolov5的yaml文件
时间: 2023-08-02 22:08:59 浏览: 64
Yolov5的配置文件(yaml文件)包含了模型的各种参数设置和训练配置。以下是一个示例的Yolov5配置文件的内容:
```yaml
# Model parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # model width multiple
# Backbone architecture
backbone:
name: yolov5s # name of the backbone architecture
c2: 3 # number of input channels (RGB images)
# Model architecture
model:
name: yolov5 # name of the YOLOv5 model architecture
ch: [128, 256, 512] # output channels for each stage
# Input image parameters
train_size: [640, 640] # training image size (width, height)
test_size: [640, 640] # testing image size (width, height)
stride: 32 # downsampling stride
# Data augmentation settings
mosaic: 1.0 # probability for applying mosaic data augmentation
mixup: 0.0 # probability for applying mixup data augmentation
# Training hyperparameters
hyp:
lr0: 0.01 # initial learning rate
lrf: 0.02 # final learning rate
momentum: 0.937 # SGD momentum
weight_decay: 0.0005 # weight decay
giou: 0.05 # GIoU loss weight
cls: 0.5 # classification loss weight
cls_pw: 1.0 # classification loss positive weight
obj: 1.0 # objectness loss weight
obj_pw: 1.0 # objectness loss positive weight
iou_t: 0.20 # IoU threshold for objectness loss gain
anchor_t: 4.0 # anchor-multiple threshold
fl_gamma: 0.0 # focal loss gamma
# Training settings
train:
epochs: 300 # number of training epochs
batch_size: 16 # batch size
img_size:
- [640, 640] # input image size for training
- [1280, 1280] # input image size for testing
rect: false # rectangular training images
resume: false # resume training from checkpoint
transfer: false # transfer learning from a previous checkpoint
cache_images: true # cache images for faster training
# Testing settings
test:
batch_size: 16 # batch size for testing
img_size: [1280, 1280] # input image size for testing
conf_thres: 0.001 # confidence threshold for object detection
iou_thres: 0.6 # IoU threshold for NMS
# Miscellaneous settings
seed: 0 # random seed
device: 'cuda' # device to use for training/testing
save_dir: 'runs/train/exp' # directory to save the model weights and results
```
这个配置文件中包含了各种参数,如模型架构、输入图像大小、数据增强设置、训练超参数、测试设置等。你可以根据自己的需求进行修改和定制。