yunlongdong / fcn-pytorch Goto Github PK
View Code? Open in Web Editor NEWAnother pytorch implementation of FCN (Fully Convolutional Networks)
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hc851930093 prakharcode muharremokutan daodaoliang auserj zhimingluo jangocheng shao15xiang adolhung zekunstevenzhang eglrp shengjunzhang douruixin botforge m-cheng1129 tclikang wenzhifang gaussian37 sunchuanxi keepmovin annie7777 jzw0025 dlml pandinosaurus inkto jet-chu abexit xubaozhao louisnizhengbin refreshen lianghe77 jianglin314 a466370683 ningmengweidexiaotanke luzhaohao jshacsg123 zhangyongshuai2020 dragon-20 wahyurahmaniar yangyin2016 hanmiaojun xrosliang shulinhe trendingtechnology sylvia6 shflhl jingligao dongso bambootuna afjun raanww 13258287065fcn-pytorch's Issues
Could you share your annotation tool? thanks！
How to use inference.py?
I can run the training phase and I've had the checkpoint file.
Now the problem is how to use the provided inference.py. In that file, I see you generate a random input and use the pretrained model. What about loading a test image and running inference on it? How to do it specifically?
Thanks.
very bad testing output
can you tell me please why this model gave me a very bad output segmented image? it trained successfully but when I am using the model to segment a new image or an image from your dataset it also gives me completely wrong output please help me
about RGB and BGR
"imgA = cv2.imread('last/'+img_name)"
my doubt is that the input data's format is BGR but VGG is compatible to RGB
你好，在FCN.py中有一行代码不是很理解，请指教
你好，在FCN.py中有一行代码不是很理解，请指教
for layer in range(begin, end): x = self.features[layer](x) # print(x.shape) output["x%d"%(idx+1)] = xx=self.featureslayer表示什么意思
Used BCE - Loss but no sigmoid activation
You have used BCE loss in your train file but I don't see you using sigmoid activation. Will that not affect anything as we use sigmoid activation when using BCE loss and Softmax when using Cross Entropy loss
onehot
请教一下 我认为您这样做会出现一个像素点label为【1，1】或【0，0】的情况啊 还有您的代码似乎做的是对通道的分类 不是对像素点的分类 比如对2242243的图片，label应该是224224n_classes 吧 但是您的是2242243*n_classes
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