Code used for the results in the paper "ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning"

Related tags

Deep Learning ClassMix
Overview

Code used for the results in the paper "ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning"

Getting started

Prerequisites

  • CUDA/CUDNN
  • Python3
  • Packages found in requirements.txt

Datasets

Cityscapes

Download the dataset from the Cityscapes dataset server(Link). Download the files named 'gtFine_trainvaltest.zip', 'leftImg8bit_trainvaltest.zip' and extract in ../data/CityScapes/

Pascal VOC 2012

Download the dataset from here. Download the file 'training/validation data' under 'Development kit' and extract in ../data/VOC2012/. For training, you will also need to download additional labels from this link, extract this directory into ../data/VOC2012.

Input arguments

Arguments related to running the script are specified from terminal and include; number of gpus to use (if >1 torch.nn.DataParalell is used), path to configuration file (see below), path to .pth file if resuming training, name of the experiment, and whether to save images during training. More details can be found in the relevant scripts.

Arguments related to the algoritms are specified in the configuration files. These include model, data, hyperparameters related to the training, and what methods to apply on unlabeled data. A full description is provided further below.

Examples

Training a model with semi-supervised learning with example config on a single gpu

python3 trainSSL.py --config ./configs/configCityscapes.json --name name_of_training

Resuming training of a model with semi-supervised learning

python3 trainSSL.py --resume path/to/checkpoint.pth --name name_of_training

Evaluating a trained model

python3 evaluateSSL.py --model-path path/to/checkpoint.pth

Pretrained model

Here is a model trained with SSL with 1/8 (372) labeled samples for Cityscapes.

Comments
  • validation mIoU is nan?

    validation mIoU is nan?

    Hi, I'm trying to run the default experiment on cityscapes with: python3 trainSSL.py --config ./configs/configSSL.json --name SSL as indicated in the readme. It seems validatoin mIoU in tensorboard is NaN any idea what could be going wrong ?

    opened by nikste 4
  • how to run it?

    how to run it?

    hi,README.md display that "python3 trainSSL.py --config ./configs/configSSL.json --name SSL",it doesn't run. so，what the true practice?maybe more detail

    opened by over-star 3
  • Bump opencv-python from 4.1.0.25 to 4.1.1.26

    Bump opencv-python from 4.1.0.25 to 4.1.1.26

    Bumps opencv-python from 4.1.0.25 to 4.1.1.26.

    Release notes

    Sourced from opencv-python's releases.

    4.1.1.26

    OpenCV version 4.1.1.

    Changes:

    • FFmpeg has been compiled with https support on Linux builds #229
    • CI build logic related changes #197, #227, #228
    • Custom libjepg-turbo removed because it's provided by OpenCV #231
    • 64-bit Qt builds are now smaller #236
    • Custom builds should be now rather easy to do locally #235:
      1. Clone this repository
      2. Optional: set up ENABLE_CONTRIB and ENABLE_HEADLESS environment variables to 1 if needed
      3. Optional: add additional Cmake arguments to CMAKE_ARGS environment variable
      4. Run python setup.py bdist_wheel
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    dependencies 
    opened by dependabot[bot] 1
  • Choice of consistency loss

    Choice of consistency loss

    Thanks for sharing of your work. I've noticed that the consistency loss CE is used in your implementation, while in many other SSL works MSE is used instead. Is there any difference between these two loss choices?

    opened by syorami 1
  • Bump numpy from 1.15.4 to 1.22.0

    Bump numpy from 1.15.4 to 1.22.0

    Bumps numpy from 1.15.4 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 0
  • Bump opencv-python from 4.1.0.25 to 4.2.0.32

    Bump opencv-python from 4.1.0.25 to 4.2.0.32

    Bumps opencv-python from 4.1.0.25 to 4.2.0.32.

    Release notes

    Sourced from opencv-python's releases.

    4.2.0.32

    OpenCV version 4.2.0.

    Changes:

    • macOS environment updated from xcode8.3 to xcode 9.4
    • macOS uses now Qt 5 instead of Qt 4
    • Nasm version updated to Docker containers
    • multibuild updated

    Fixes:

    • don't use deprecated brew tap-pin, instead refer to the full package name when installing #267
    • replace get_config_var() with get_config_vars() in setup.py #274
    • add workaround for DLL errors in Windows Server #264

    4.1.2.30

    OpenCV version 4.1.2.

    Changes:

    • Python 3.8 builds added to the build matrix
    • Support for Python 3.4 builds dropped (Python 3.4 is in EOL)
    • multibuild updated
    • minor build logic changes
    • Docker images rebuilt

    Notes:

    Please note that Python 2.7 enters into EOL phase in January 2020. opencv-python Python 2.7 wheels won't be provided after that.

    4.1.1.26

    OpenCV version 4.1.1.

    Changes:

    ... (truncated)

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  • Why my validation mIOU is much lower than paper?

    Why my validation mIOU is much lower than paper?

    Hi，constrainted to my GPUs，i am using 384×768 as input size，using coco pre-trained model and cityscapes dataset. Other config parameters are defalut. After 50000 iterations，the miou reslult is still about 0.16 could you help me??? thanks :) @WilhelmT

    opened by bei181 2
  • OHEM does Not work

    OHEM does Not work

    hi, Its really a nice work.

    ClassMix does NOT work when I changed supervised loss function from cross-entropy to OHEM, the mean-IoU on eval dataset seems like really close, I dont know why this happen?

    opened by gbyy422990 1
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