datatap-python
coral-cnn
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| datatap-python | coral-cnn | |
|---|---|---|
| 9 | 4 | |
| 34 | 323 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| over 1 year ago | almost 3 years ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
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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.
datatap-python
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[Project] DataTap provides droplets ( containers for datasets) to make working on popular deep learning datasets easy.
Learn more about how you can start using this here https://github.com/zensors/datatap-python
- Stream any deep learning dataset with just 3 lines of code into Pytorch, Tensorflow or any python project.
- Data droplets make dataset management & sharing simple -- The dataTap Python library is the primary interface for using dataTap's rich data management tools. Create datasets, stream annotations, and analyze model performance all with one library.
- Data droplets specification lets you unify and easily share deep learning datasets. Doplets are designed for complex annotations and let you focus on Deep learning rather than data manipulation.
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The fastest format to store, access & manage labelled data for any deep learning project
http://datatap.dev/ is an open source platform that allows you to easily pull in any data set in a standard format so you can start training a deep learning model in < 3 minutes
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Setting up a feedback loop for performance evaluation and retraining of a model.
You should import the data into https://github.com/zensors/datatap-python, will make managing data for the feedback loop easier
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Show HN: Free user-friendly platform for visual data management
Looking for a user-friendly data management tool? With DataTap, you focus on algorithm design, not on data wrangling. DataTap is a visual data management platform from Zensors.
Check out the repository (https://github.com/zensors/datatap-python)
The dataTap Python library is the primary interface for using dataTap's rich data management tools. Create datasets, stream annotations, and analyze model performance all with one library.
Cool Features
coral-cnn
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[D] Why is Ordinal Regression so overlooked?
The most recent and usable DL attempt I have found is the CORAL/CORN frameworks (keras, pytorch) which have just a few stars, and that's it.
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[D] can regression models be used for ranking?
To your question, there are specific types of models called ordinal regression / ordinal classification models that do not assume a metric distance between values. E.g., if you have "20/hr, $15/hr, $0/hr" these models don't assume that the distance between 0 and 15 is 3x the distance between 20 and 15. It just assumes 20 > 15 > 0. We worked on this a bit in the context of neural networks: https://www.sciencedirect.com/science/article/pii/S016786552030413X , https://raschka-research-group.github.io/coral_pytorch/
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[D] Modeling class errors
If you are interested, I recently worked on a simple ordinal regression approach for neural networks here: https://www.sciencedirect.com/science/article/pii/S016786552030413X
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[R] [D] What machine learning methods can be used for ordinal regression?
Just took a quick look at that paper, it sounds like a good approach. If you are interested, we recently developed an ordinal regression approach with implementation in PyTorch (https://github.com/Raschka-research-group/coral-cnn). Someone also recently ported it to Keras: https://github.com/ck37/coral-ordinal. I haven't read the paper you mentioned in detail, but it seems our method is similar except that we add the probabilities that are >0.5 and that we have theoretical guarantees. rank consistency.
What are some alternatives?
simpleT5 - simpleT5 is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models.
coral-ordinal - Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)
whylogs - An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
contrastive-unpaired-translation - Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
seq2seq - A general-purpose encoder-decoder framework for Tensorflow
PyTorchZeroToAll - Simple PyTorch Tutorials Zero to ALL!
iterative-stratification - scikit-learn cross validators for iterative stratification of multilabel data
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
Schematics - Python Data Structures for Humans™.
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. [Moved to: https://github.com/horovod/horovod]
analog-watch-recognition - Reading time from analog clocks