caffe随记(九)---利用FCN和已有的model进行图像语义分割 1、下载caffemodel 本例中我们使用的是pascalcontext-fcn32的模型,这个下载链接在它的文件夹里有,就是url那个文件 下载pascalcontext-fcn32s-heavy.caffemodel 这个文件 2、增加python的路径 本次我使用的方法是 vim ~/.b
Furthermore, the deep neural network FCN is used to build the base model of semantic segmentation. The ASPP, data augmentation, SENet, decoder and global pooling are added to the baseline to optimize the model structure and improve the effect of semantic segmentation. Finally, the more accurate...
Model a simple Anti-Lock Braking System (ABS). The model simulates the dynamic behavior of a vehicle under hard braking conditions. The model represents a single wheel, which may be replicated a number of times to create a model for a multi-wheel vehicle. ...
[1]http://home.ustc.edu.cn/~liujunyan/blog/fcn/ [2]Nucleus image segmentation method based on GAN and FCN model [3]https://blog.csdn.net/m0_56192771/article/details/124113078 [4]https://www.wandouip.com/t5i87567/
#定义模型,并加载预训练 model = fcn_resnet50(pretrained=True) # 默认classes是21,如果不是21,则要修改类别。 if num_classes != 21: model.classifier[4] = torch.nn.Conv2d(512, num_classes, kernel_size=(1, 1), stride=(1, 1)) model.aux_classifier[4] = torch.nn.Conv2d(256, num_cla...
Model a simple Anti-Lock Braking System (ABS). The model simulates the dynamic behavior of a vehicle under hard braking conditions. The model represents a single wheel, which may be replicated a number of times to create a model for a multi-wheel vehicle. ...
1. 转换caffe model为Chainer model ./scripts/caffe_to_chainermodel.py 2. load model,进行分割 ./scripts/fcn_forward.py –img-files data/pascal/VOC2012/JPEGImages/2007_000129.jpg 训练自己的数据 这个前后搞了快一个月,才把最终的训练搞定,其中艰辛很多,在这里写出来供大家参考 ...
转换caffe model为Chainer model ./scripts/caffe_to_chainermodel.py load model,进行分割 ./scripts/fcn_forward.py –img-files data/pascal/VOC2012/JPEGImages/2007_000129.jpg 训练自己的数据 这个前后搞了快一个月,才把最终的训练搞定,其中艰辛很多,在这里写出来供大家参考 ...
importtorch.optimasoptim# 定义损失函数criterion=torch.nn.CrossEntropyLoss()# 定义优化器optimizer=optim.Adam(model.parameters(),lr=0.001) 1. 2. 3. 4. 5. 6. 7. 5. 训练模型 如今一切准备就绪,可以开始训练模型: model.train()# 将模型设置为训练模式# 训练过程forepochinrange(num_epochs):forimage...
model = torch.nn.DataParallel(model,device_ids=[0,1]) 如果使用了多显卡,再使用模型的参数就需要改为model.module.xxx,例如: 深色代码主题 复制 params = [pforpinmodel.module.aux_classifier.parameters()ifp.requires_grad]params_to_optimize.append({"params": params,"lr": args.lr*10}) ...