research-seq2seq-HTR
kandi X-RAY | research-seq2seq-HTR Summary
kandi X-RAY | research-seq2seq-HTR Summary
research-seq2seq-HTR is a Shell library. research-seq2seq-HTR has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
research-seq2seq-HTR
research-seq2seq-HTR
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Quality
Security
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Support
research-seq2seq-HTR has a low active ecosystem.
It has 11 star(s) with 11 fork(s). There are 5 watchers for this library.
It had no major release in the last 6 months.
There are 4 open issues and 2 have been closed. On average issues are closed in 11 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of research-seq2seq-HTR is current.
Quality
research-seq2seq-HTR has no bugs reported.
Security
research-seq2seq-HTR has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
research-seq2seq-HTR 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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research-seq2seq-HTR releases are not available. You will need to build from source code and install.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of research-seq2seq-HTR
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of research-seq2seq-HTR
research-seq2seq-HTR Key Features
No Key Features are available at this moment for research-seq2seq-HTR.
research-seq2seq-HTR Examples and Code Snippets
No Code Snippets are available at this moment for research-seq2seq-HTR.
Community Discussions
No Community Discussions are available at this moment for research-seq2seq-HTR.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install research-seq2seq-HTR
You can download it from GitHub.
Support
If an out of memory error occurs, you could change a smaller batch size and retrain it. The BATCH_SIZE can be modified in main_torch_latest.py.
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