Comments (12)
@FuriouslyCurious
Here is the code of the model. Hopefully, it helps your research.
https://gist.github.com/felixgwu/045c887b6ccdf0edf4648da0c40bcc12
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Hi, @FuriouslyCurious I have an implementation of FC-DenseNet at head already.
I can submit a PR is needed.
However, I couldn't match their reported mIoU and accuracy by training from scratch.
I've submitted an issue to their repo, but he is busy to help these days.
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If you want to use sub-pixel convolution for upscaling you can use the PixelShuffle
layer in PyTorch
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Haha who can say no to that 😄
The original implementation is in Theano. We could probably use the saved weights for that model, but it may be better to train from scratch.
It might make sense to hold off until the CamVid dataset is added (#90).
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Thank you @gpleiss and @felixgwu
I am planning to train a Dense FC model from scratch using medical data and publish the weights / trained model for medical research.
@felixgwu if you share the code in a GIST I will love to try it out for some experiments I am running.
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Thank you @felixgwu !
@ycszen FYI: DenseNet FCN code in GIST above ^
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Thank you! @FuriouslyCurious
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How can i import the saved weight in the original implementation in Theano to the pytorch with the same architechture? @felixgwu
As I know, the saved weight is numpy format. and it seem difficult to initial the weight in pytorch one by one, because the net is so deep. Thank you for advanced
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@felixgwu Do you remember how far you where off from the original reported mIoU and accuracy by training from scratch?
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I found your results in FC-DenseNet issue 11.
Did you manage to further improve the results?
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@EliasVansteenkiste and @felixgwu Check out the Keras Tiramisu implementation below: Developer @titu1994 used SubPixelConvolution instead of Deconvolution as default method for upsampling. Not sure if that helps accuracy, but worth trying.
https://github.com/titu1994/Fully-Connected-DenseNets-Semantic-Segmentation
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@felixgwu . Thank you for you code implementation of FC-DenseNet in pytorch. Recent, i want to reproduce the result about FC-DenseNet, but my loss does not converge at all. would you share me you dataLoader so that i can compare it with my code? and if i initialize the learning rate with 1e-3, the loss will be infinity. I am so puzzled with it .
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Related Issues (20)
- Image scaling is performed incorrectly (in all detection models!) HOT 5
- IndexError: index 168 is out of bounds for dimension 0 with size 168 in keypointrcnn_loss HOT 1
- Nightly build flaky pytorch/vision / conda-py3_11-cpu builds HOT 1
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- Mypy job is broken
- Regarding IMAGENET1K_V1 and IMAGENET1K_V2 weights
- Compiling resize_image: function interpolate not_implemented HOT 1
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- Run all torchvision models in one script. HOT 1
- Build fails: error: unknown type name 'j_decompress_ptr' HOT 3
- Differences in CPU vs CUDA resize for uint8 images HOT 2
- Enable Video models for other tasks
- Can't use gaussian_blur if sigma is a tensor on gpu HOT 2
- Mask r-cnn training runs infinitely without output or error HOT 1
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