pytorch-semseg | Semantic Segmentation Architectures Implemented in PyTorch | Machine Learning library

 by   meetps Python Version: v0.1.2 License: MIT

kandi X-RAY | pytorch-semseg Summary

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 download it from GitHub.

Semantic Segmentation Architectures Implemented in PyTorch
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            kandi-support Support

              pytorch-semseg has a medium active ecosystem.
              It has 3304 star(s) with 810 fork(s). There are 109 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 125 open issues and 96 have been closed. On average issues are closed in 53 days. There are 6 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pytorch-semseg is v0.1.2

            kandi-Quality Quality

              pytorch-semseg has no bugs reported.

            kandi-Security Security

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

            kandi-License 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.

            kandi-Reuse Reuse

              pytorch-semseg releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pytorch-semseg and discovered the below as its top functions. This is intended to give you an instant insight into pytorch-semseg implemented functionality, and help decide if they suit your requirements.
            • Load pretrained pretrained model .
            • Train the model .
            • Validate the trained model .
            • Setup pre - encodings .
            • Initialize the model .
            • Get a model instance .
            • Initialize VGG16 params .
            • Resize image .
            • Return a scheduler .
            • Randomly crop the image .
            Get all kandi verified functions for this library.

            pytorch-semseg Key Features

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

            pytorch-semseg Examples and Code Snippets

            copy iconCopy
            # 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
            Pythondot img2Lines of Code : 7dot img2License : Permissive (MIT)
            copy iconCopy
            @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
            Pythondot img3Lines of Code : 6dot img3License : Permissive (MIT)
            copy iconCopy
            @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

            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 download it from GitHub.
            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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