Deeplabv3_pytorch_Cityscape_and_Apolloscape

by JinyongJeong Python Version: Current License: MIT

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kandi X-RAY | Deeplabv3_pytorch_Cityscape_and_Apolloscape Summary

Deeplabv3_pytorch_Cityscape_and_Apolloscape is a Python library. Deeplabv3_pytorch_Cityscape_and_Apolloscape has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Deeplabv3_pytorch_Cityscape_and_Apolloscape build file is not available. You can download it from GitHub.

Deeplabv3_pytorch_Cityscape_and_Apolloscape

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Deeplabv3_pytorch_Cityscape_and_Apolloscape has a low active ecosystem.

It has 3 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

Deeplabv3_pytorch_Cityscape_and_Apolloscape has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Deeplabv3_pytorch_Cityscape_and_Apolloscape is current.

Quality

Deeplabv3_pytorch_Cityscape_and_Apolloscape has no bugs reported.

Security

Deeplabv3_pytorch_Cityscape_and_Apolloscape has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Deeplabv3_pytorch_Cityscape_and_Apolloscape is licensed under the MIT License. This license is Permissive.

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

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Deeplabv3_pytorch_Cityscape_and_Apolloscape releases are not available. You will need to build from source code and install.

Deeplabv3_pytorch_Cityscape_and_Apolloscape has no build file. You will be need to create the build yourself to 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 Deeplabv3_pytorch_Cityscape_and_Apolloscape and discovered the below as its top functions. This is intended to give you an instant insight into Deeplabv3_pytorch_Cityscape_and_Apolloscape implemented functionality, and help decide if they suit your requirements.

Callback called when an image is received
Convert an image to a color image
Convert label image to color
Add weight decay
Listen for image color events
Get epoch from checkpoint name

Get all kandi verified functions for this library.

Deeplabv3_pytorch_Cityscape_and_Apolloscape Key Features

No Key Features are available at this moment for Deeplabv3_pytorch_Cityscape_and_Apolloscape.

Deeplabv3_pytorch_Cityscape_and_Apolloscape Examples and Code Snippets

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Vulnerabilities

No vulnerabilities reported

Install Deeplabv3_pytorch_Cityscape_and_Apolloscape

You can download it from GitHub.
You can use Deeplabv3_pytorch_Cityscape_and_Apolloscape 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.

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model/resnet.py:Definition of the custom Resnet model (output stride = 8 or 16) which is the backbone of DeepLabV3.model/aspp.py:Definition of the Atrous Spatial Pyramid Pooling (ASPP) module.model/deeplabv3.py:Definition of the complete DeepLabV3 model.utils/preprocess_data.py:Converts all Cityscapes label images from having Id to having trainId pixel values, and saves these to deeplabv3/data/cityscapes/meta/label_imgs. Also computes class weights according to the ENet paper and saves these to deeplabv3/data/cityscapes/meta.utils/utils.py:Contains helper funtions which are imported and utilized in multiple files.datasets.py:Contains all utilized dataset definitions.

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