ssd.pytorch | SSD & S3FD & RefineDet

by ryzejiang Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | ssd.pytorch Summary

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

SSD & S3FD & RefineDet

Support

Quality

Security

License

Reuse

Support

ssd.pytorch has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

ssd.pytorch has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ssd.pytorch is current.

Quality

ssd.pytorch has no bugs reported.

Security

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

License

ssd.pytorch 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

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

ssd.pytorch has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

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ssd.pytorch Key Features

No Key Features are available at this moment for ssd.pytorch.

ssd.pytorch Examples and Code Snippets

No Code Snippets are available at this moment for ssd.pytorch.

Community Discussions

No Community Discussions are available at this moment for ssd.pytorch.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install ssd.pytorch

Install PyTorch by selecting your environment on the website and running the appropriate command.
Clone this repository. Note: We currently only support Python 3+.
Then download the dataset by following the instructions below.
We now support Visdom for real-time loss visualization during training! To use Visdom in the browser: # First install Python server and client pip install visdom # Start the server (probably in a screen or tmux) python -m visdom.server Then (during training) navigate to http://localhost:8097/ (see the Train section below for training details).
Note: For training, we currently support VOC and COCO, and aim to add ImageNet support soon.

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 .

Find more information at: