parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path') parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=300)parser.add_argument('--batch-siz...
# --json_path 输入的json文件路径 # --save_path 保存的文件夹名字,默认为当前目录下的labels。 import os import json from tqdm import tqdm import argparse parser = argparse.ArgumentParser() # 这里根据自己的json文件位置,换成自己的就行 parser.add_argument('--json_path', default='/home/ubuntu/c...
parser.add_argument('--data', type=str, default='data/test.yaml', help='data.yaml path') # 超参 parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path') # batch size,根据gpu配置设置 parser.add_argument('--batch-size', type=int, d...
dataset = LoadImagesAndLabels(path, imgsz, batch_size, augment=augment, # augment images hyp=hyp, # augmentation hyperparameters rect=rect, # rectangular training cache_images=cache, single_cls=opt.single_cls, stride=int(stride), pad=pad, image_weights=image_weights, prefix=prefix) 下面是LoadI...
--weights, initial weights path (default value:'yolo7.pt') --cfg, model.yaml path (default value:'') --data, data.yaml path (default value:'data/coco.yaml') --hyp, hyperparameters path (default value:'data/hyp.scratch.p5.yaml') ...
(path, imgsz, batch_size,augment=augment, # augment imageshyp=hyp, # augmentation hyperparametersrect=rect, # rectangular trainingcache_images=cache,single_cls=opt.single_cls,stride=int(stride),pad=pad,image_weights=image_weights,prefix=prefix)batch_size = min(batch_size, len(dataset))nw = ...
Optimal hyper-parameters:借助遗传算法选择最优超参数 Random taining Shapes Mish activation:同上 SPP-block:同上 SAM-block PAN path-aggregation block:同上 DIoU-NMS:包含了两个box之间中心点之间的距离,在最后做NMS的时候,也可以将box之间的中心点距离加入到计算中。实验结果显示对于遮挡情况鲁棒性非常强...
The hyperparameter configuration file helps us define the hyperparameters for our neural network. This gives us a lot of control over the behavior of our model training, and more advanced users should consider modifying certain values like the learning rate if training is not proceeding as desired...
('--data', type=str, default='data/mydata.yaml', help='data.yaml path')parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path')parser.add_argument('--epochs', type=int, default=300)parser.add_argument('--batch-size', type=int, ...
model.hyp = hyp # attach hyperparameters to model model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou) model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights 1. 2. 3. 4.