Another pytorch implementation of FCN (Fully Convolutional Networks)

Related tags

Deep Learning semantic-segmentation pytorch-implmention
Overview

FCN-pytorch-easiest

Trying to be the easiest FCN pytorch implementation and just in a get and use fashion

Here I use a handbag semantic segmentation for illustration on how to train FCN on your own dataset and just go to use. To train on your own dataset you just need to see in BagData.py which implements a dataloader in pytorch. What you actually need to do is providing the images file and the correspoding mask images. And for visualization in the training process I use visdom.

requirement

I have tested the code in pytorch 0.3.0.post4 in anaconda python 3.6 in ubuntu 14.04 with GTX1080 in cuda8.0

train

here three images pair is provided in folder last/ and last_msk/ . Here I want to do a handbag semantic segmentation which is stated as belows.

task

Firstly because visdom is used to visualize the training process, you need open another terminal and run

python -m visdom.server

Then you run in another terminal

python FCN.py

You can open your browser and goto localhost:8097 to see the visulization as following the first row is the prediction.

vis

deploy

and for deploy and inference I also provide a script inference.py. You should be careful about the model path. Bacause I did not provide the trained weights file. :-P

BTW, FCN.py is copy from other repo.

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Comments
  • How to use inference.py?

    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.

    opened by tranvnhan 3
  • Used BCE - Loss but no sigmoid activation

    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

    opened by Gunnvant 0
  • very bad testing output

    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

    opened by RokiaAbdeen 1
  • 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

    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

    opened by rw1995 3
Owner
Y. Dong
HUST Ph.D.
Y. Dong
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