code-generator
Web Application to generate your training scripts with PyTorch Ignite (by pytorch-ignite)
receptive_field_analysis_toolbox
A toolbox for receptive field analysis and visualizing neural network architectures (by MLRichter)
| code-generator | receptive_field_analysis_toolbox | |
|---|---|---|
| 1 | 2 | |
| 38 | 109 | |
| - | - | |
| 7.4 | 0.0 | |
| 25 days ago | 27 days ago | |
| Vue | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
code-generator
Posts with mentions or reviews of code-generator.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-08-31.
-
Introducing PyTorch-Ignite's Code Generator v0.2.0
Along with the PyTorch-Ignite 0.4.5 release, we are excited to announce the new release of the web application for generating PyTorch-Ignite's training pipelines. This blog post is an overview of the key features and updates of the Code Generator v0.2.0 project release.
receptive_field_analysis_toolbox
Posts with mentions or reviews of receptive_field_analysis_toolbox.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[D] Is neural network architecture just "alchemy"?
On a more personal note, the interaction of input resolution and receptive field allows you to pretty accurately determine if your network is too deep. I created an OpenSource-library for people to check this out: https://github.com/MLRichter/receptive_field_analysis_toolbox. Also, I found out in this publication that the intrinsic dimensionality of the data inside a layer can be analyzed in life during training pretty efficiently and used as a guideline to adjust the width of the network. So, there are some ways to guide neural architecture design and there maybe are more to come, but that's just me being optimistic about my own research.
- GitHub - MLRichter/receptive_field_analysis_toolbox
What are some alternatives?
When comparing code-generator and receptive_field_analysis_toolbox you can also consider the following projects:
BigGAN-PyTorch - The author's officially unofficial PyTorch BigGAN implementation.
pytest-visual - A visual testing framework for ML with automated change detection
ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
traingenerator - 🧙 A web app to generate template code for machine learning