coral-cnn
datatap-python
| coral-cnn | datatap-python | |
|---|---|---|
| 4 | 9 | |
| 330 | 34 | |
| 2.1% | - | |
| 0.0 | 0.0 | |
| about 3 years ago | almost 2 years ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
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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.
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
What are some alternatives?
coral-ordinal - Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)
simpleT5 - simpleT5 is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models.
contrastive-unpaired-translation - Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
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. 📈
PyTorchZeroToAll - Simple PyTorch Tutorials Zero to ALL!
seq2seq - A general-purpose encoder-decoder framework for Tensorflow
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
iterative-stratification - scikit-learn cross validators for iterative stratification of multilabel data
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. [Moved to: https://github.com/horovod/horovod]
Schematics - Python Data Structures for Humans™.
analog-watch-recognition - Reading time from analog clocks