pytorch-semseg | Semantic Segmentation Architectures | Machine Learning library

by meetshah1995 Python Version: 0.1.2 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | pytorch-semseg Summary

pytorch-semseg is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. pytorch-semseg has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install pytorch-semseg' or download it from GitHub, PyPI.

Semantic Segmentation Architectures Implemented in PyTorch

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pytorch-semseg has a medium active ecosystem.

It has 3054 star(s) with 783 fork(s). There are 104 watchers for this library.

It had no major release in the last 12 months.

There are 119 open issues and 96 have been closed. On average issues are closed in 53 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch-semseg is 0.1.2

Quality

pytorch-semseg has 0 bugs and 0 code smells.

Security

pytorch-semseg has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pytorch-semseg code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch-semseg is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

pytorch-semseg releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

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pytorch-semseg Key Features

No Key Features are available at this moment for pytorch-semseg.

pytorch-semseg Examples and Code Snippets

simple pytorch segmentation tutorial with apex mixed precision and distributed training

Python

Lines of Code : 8

License : Permissive (MIT)

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# train on 4 GPUs
python -m torch.distributed.launch --nproc_per_node=4 train.py --config configs/cityscape_drn_c_26.json

# evaluate
python evaluate.py --logdir [run logdir] [-s] 

# Moreover, you can add [your configs].json in run_tasks.sh
sh run_t

Recurrent U-Net for Resource Constraint Segmentation

Python

Lines of Code : 7

License : Permissive (MIT)

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@inproceedings{wang2019recurrent,
  title={Recurrent U-Net for resource-constrained segmentation},
  author={Wang, wei and Yu, Kaicheng and Hugonot, Joachim and Fua, Pascal and Salzmann, Mathieu},
  booktitle={Proceedings of the IEEE International Co

MultiDepth

Python

Lines of Code : 6

License : Permissive (MIT)

Copy

@InProceedings{,
  author    = {Lukas Liebel and Marco K\"orner},
  title     = {{MultiDepth}: Single-Image Depth Estimation via Multi-Task Regression and Classification},
  booktitle = {IEEE Intelligent Transportation Systems Conference (ITSC)},
  y

Community Discussions

Trending Discussions on pytorch-semseg

Why does this semantic segmentation network have no softmax classification layer in Pytorch?

QUESTION

Why does this semantic segmentation network have no softmax classification layer in Pytorch?

Asked 2019-Jan-08 at 06:20

I am trying to use the following CNN architecture for semantic pixel classification. The code I am using is here

However, from my understanding this type of semantic segmentation network typically should have a softmax output layer for producing the classification result.

I could not find softmax used anywhere within the script. Here is the paper I am reading on this segmentation architecture. From Figure 2, I am seeing softmax being used. Hence I would like to find out why this is missing in the script. Any insight is welcome.

...

ANSWER

Answered 2019-Jan-08 at 06:20

You are using quite a complex code to do the training/inference. But if you dig a little you'll see that the loss functions are implemented here and your model is actually trained using cross_entropy loss. Looking at the doc:

This criterion combines log_softmax and nll_loss in a single function.

For numerical stability it is better to "absorb" the softmax into the loss function and not to explicitly compute it by the model.
This is quite a common practice having the model outputs "raw" predictions (aka "logits") and then letting the loss (aka criterion) do the softmax internally. If you really need the probabilities you can add a softmax on top when deploying your model.

Source https://stackoverflow.com/questions/54083220

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pytorch-semseg

You can install using 'pip install pytorch-semseg' or download it from GitHub, PyPI.
You can use pytorch-semseg like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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