Comments (5)
您好大佬,打扰一下,我也遇到了同样的问题,请问用那几个py文件可以训练模型
VAE_GAN_train.py
文件用于对VAE-GAN进行训练,如果你不需要改变网络的输入图片尺寸(即512x512x1的单通道灰度图),那么于第31行:
"""从'./cut_imgs/目录中读取所有图片并加载成数据集用于训练'"""
image_directory = "./cut_imgs/"
设置成你样本所在的目录,就会在训练时这个目录载入图像作为训练样本。
测试样本需要到 GenerateTestDataset.py
文件中,然后在第八行:
def load_test_dataset(test_imgs_dir='./test_imgs/'):
路径参数中设定好你的测试图片样本目录,运行这个文件,就会读取图片并且把图片保存为数据集的形式:
保存路径位于第49行:
test_dataset_path = './datasets/test_dataset.pt'
在设定好这些以后,运行 VAE_GAN_train.py
文件,即可开始对VAE-GAN网络的训练,你会得到辨别器权重文件 Discriminator.pth
以及生成器(VAE)的权重文件 VAE.pth
,使用辨别器在SAM分割图像后找到你需要的目标内容,使用VAE来进行样本的再生成。
预测流程我在另一个issue里回答过,有时间的话我整理到Readme里。
from unsupervised-defect-detection-project-based-on-vae-gan-architecture.
你需要根据你的数据集去训练一个,我们的那个权重文件太大了传不上来,而且只能用在我们的项目情况(五种类型的PCB缺陷检测)里,但是我可以说明一下用到的三个权重文件:
sam_vit_l_0b3195.pth
:这个是SAM
分割一切官方提供的权重模型,你可以去他们的项目里找到,大小1.16GB,如果有需要 @Lemon-33 你可以向他要下载链接/下载方式。Discriminator_trained.pth
:使用VAE-GAN
模型训练得到的辨别器模型,用于和SAM
配合,判断分割出来的图片部分是否正确。VAE.pth
:使用VAE-GAN
模型训练得到的生成器模型,也可以单独训练VAE
模型得到(效果略差),用于图像重建,不管输入的图片是否有缺陷,都会重建出正确图片为后续缺陷检测作为参照。
from unsupervised-defect-detection-project-based-on-vae-gan-architecture.
您好大佬,打扰一下,我也遇到了同样的问题,请问用那几个py文件可以训练模型
from unsupervised-defect-detection-project-based-on-vae-gan-architecture.
您好大佬,打扰一下,我也遇到了同样的问题,请问用那几个py文件可以训练模型
https://github.com/facebookresearch/segment-anything?tab=readme-ov-file#model-checkpoints 可以在这个项目里下载一下pth文件
from unsupervised-defect-detection-project-based-on-vae-gan-architecture.
项目运行需要很多的pth文件,如何获取?
https://github.com/facebookresearch/segment-anything?tab=readme-ov-file#model-checkpoints 可以在这个项目里下载一下pth文件
from unsupervised-defect-detection-project-based-on-vae-gan-architecture.
Related Issues (2)
- 环境依赖及一些问题 HOT 1
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from unsupervised-defect-detection-project-based-on-vae-gan-architecture.