UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset

Related tags

Deep Learning pytorch image-segmentation
Overview

TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation

By Vladimir Iglovikov and Alexey Shvets

Introduction

TernausNet is a modification of the celebrated UNet architecture that is widely used for binary Image Segmentation. For more details, please refer to our arXiv paper.

UNet11

loss_curve

Pre-trained encoder speeds up convergence even on the datasets with a different semantic features. Above curve shows validation Jaccard Index (IOU) as a function of epochs for Aerial Imagery

This architecture was a part of the winning solutiuon (1st out of 735 teams) in the Carvana Image Masking Challenge.

Installation

pip install ternausnet

Citing TernausNet

Please cite TernausNet in your publications if it helps your research:

@ARTICLE{arXiv:1801.05746,
         author = {V. Iglovikov and A. Shvets},
          title = {TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation},
        journal = {ArXiv e-prints},
         eprint = {1801.05746},
           year = 2018
        }

Example of the train and test pipeline

https://github.com/ternaus/robot-surgery-segmentation

Comments
  • training example

    training example

    Hello, Thank you for this upload and congrats on winning the Kaggle challenge. Is it possible for you to provide an example of how to train the network? I can't seem to put everything just right for it to run.

    opened by pmcrodrigues 3
  • 'from unet_models import unet11' reports error

    'from unet_models import unet11' reports error

    Hello, authors, Thanks to your sharing code and paper. I read 'Example.ipynb' then implement related codes. When I type 'from unet_models import unet11', I get an error 'File "unet_models.py", line 16 def init(self, in_: int, out: int): SyntaxError: invalid syntax'

    How can I fix it? My Pytorch version is 0.1.12.

    opened by shawnyuen 1
  • Fix nn.Upsample deprecation warning

    Fix nn.Upsample deprecation warning

    nn.Upsample causes the warning: "nn.Upsample is deprecated. Use nn.functional.interpolate instead."

    proof: https://github.com/pytorch/pytorch/blob/862b8cae51321a16936d87567e8a455186ca2d85/torch/nn/modules/upsampling.py#L129

    https://pytorch.org/docs/stable/_modules/torch/nn/functional.html#upsample

    opened by pokidyshev 0
  • Update to PyTorch 0.4

    Update to PyTorch 0.4

    • Removedvariable

    • replaced volatile=True with with torch.no_grad():

    • replaced cuda with to(device)

      Unfortunately to does not appear to support non_blocking/async.

    opened by gothos-folly 0
  • About num_classes

    About num_classes

    Hello, @ternaus! Thanks for your code, it's really cool, but meaning of num_classes, for instance, in Unet16, isn't clear.

    If I have car and background then I consider that image has 2 num_classes: 0 - background and 1 - car. But according to your code, in my example, you suppose that image has only one class. Why?

    Thanks in advance.

    opened by Oktai15 0
  • Sourcery Starbot ⭐ refactored ternaus/TernausNet

    Sourcery Starbot ⭐ refactored ternaus/TernausNet

    Thanks for starring sourcery-ai/sourcery ✨ 🌟 ✨

    Here's your pull request refactoring your most popular Python repo.

    If you want Sourcery to refactor all your Python repos and incoming pull requests install our bot.

    Review changes via command line

    To manually merge these changes, make sure you're on the master branch, then run:

    git fetch https://github.com/sourcery-ai-bot/TernausNet master
git merge --ff-only FETCH_HEAD
git reset HEAD^
    opened by sourcery-ai-bot 1
  • what is the difference between UNet11 and UNet16

    what is the difference between UNet11 and UNet16

    Hello, I want to ask what is the difference between UNet11 and UNet16. I only find that their channel numbers may be different，I hope you will kindly reply me

    opened by yearep7 0
  • sigmoid output?

    sigmoid output?

    Your paper indicates a final sigmoid output layer, but the models in model.py do not have such a layer and the outputs are not in the range [0,1]. Do I just add a torch.sigmoid in the forward call? In a somewhat unrelated matter, it doesn't seem that your loss function is available. I'm new to machine learning/pytorch/computers and any help would be appreciated. Thank you.

    opened by godsmokescrack 0
  • Change VGG11_2D to VGG11_3D

    Change VGG11_2D to VGG11_3D

    I followed this code, everything good for 2D model. Now, I want to use it for 3D model. I changed 2D to 3D, example: self.pool = nn.MaxPool2d(2,2) => self.pool = nn.MaxPool3d(2,2) ....... but, at the line 49 ( the picture); : models.vgg11(pretrained=pretrained).features is 2D model. I want to use vgg11 for 3D. How do I do? Thank you image

    p/s: I visualizered vgg11; it is 2D model image

    opened by tphankr 0
Owner
Vladimir Iglovikov
Ph.D. in Physics. Kaggle GrandMaster. Veteran of Russian Spetsnaz. Co-creator of Albumentations.
Vladimir Iglovikov
GitHub https://arxiv.org/abs/1801.05746
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