cppdep
piqa
| cppdep | piqa | |
|---|---|---|
| - | 4 | |
| 144 | 423 | |
| 0.0% | 0.0% | |
| 0.0 | 5.7 | |
| over 3 years ago | almost 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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.
cppdep
We haven't tracked posts mentioning cppdep yet.
Tracking mentions began in Dec 2020.
piqa
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What's the best strategy to get contributors and stars on a repository?
One good solution is to find someone with a problem (e.g. on Stack Overflow, Stack Exchange, Reddit, or even GitHub issues) that your project solves. That is what I did for a Python library I implemented (piqa), and people seemed to like it.
- PIQA, my first open-source project, reached 100 stars on GitHub today !
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What are some of your gold standard Python open source repos you discovered here or elsewhere that have very high quality, commented and understandable code that use best practices?
I'm currently developing piqa a Python package for Image Quality Assessment and I really try to make the code concise, readable and understandable. Have a look 😉
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🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
I see there is no image quality library (IQA) library in your list. I've made one for PyTorch - piqa - recently. It is still an early project, but there are already a few very useful metrics.
What are some alternatives?
repometascore - repometascore (aka repository metadata scoring) analyzes metadata of the given repository, collects info about its contributors, and outputs the risk level.
image-similarity-measures - :chart_with_upwards_trend: Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows: RMSE, PSNR, SSIM, ISSM, FSIM, SRE, SAM, and UIQ.
Android-multimodule-dependency-graph - Creates a dependency graph for an Android multimodule project.
colorama - Simple cross-platform colored terminal text in Python
SDMetrics - Metrics to evaluate quality and efficacy of synthetic datasets.
FUnIE-GAN - Fast underwater image enhancement for Improved Visual Perception. #TensorFlow #PyTorch #RAL2020