optuna VS cleanlab

I didn’t even know WandB did hyperparameter optimization, I figured it was a neural network visualizer based on 2 minute papers. Didn’t seem like many alternatives out there to Optuna with TPE + persistence in conditional continuous & discrete spaces.

Anyway, it’s doable to make a multi objective decide_to_prune function with Optuna, here’s an example https://github.com/optuna/optuna/issues/3450#issuecomment-19...

The network configuration process is usually not particularly scientific and mostly relies on empirical observation. For some cases, tools like Optuna can be used to automatically find the optimal parameters. In others, on others, you can look for modern studies which explore the effect of this parameter on performance, such as this study (2022), but these are typically very specific to one particular architecture.

Keras Tuner, Optuna : https://github.com/optuna/optuna ?

I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org Optuna - A hyperparameter optimization framework

Research the type of model to get an idea of what hyper parameters to use. I recommend using a hyper parameter optimization library like Optuna to get the best configuration

You can use Optuna, SMAC or hyperopt

We have feely open-sourced our new method for improving segmentation data, published a paper on the research behind it, and released a 5-min code tutorial. You can also read more in the blog if you'd like.

We’ve open-sourced one line of code to find errors in any object detection dataset via Cleanlab Object Detection, which can utilize any existing object detection model you’ve trained.

If you'd like to learn more, you can check out the blogpost, research paper, code, and tutorial to run this on your data.

Sharing some context here: in grad school, I spent months writing custom data analysis code and training ML models to find errors in large-scale datasets like ImageNet, work that eventually resulted in this paper (https://arxiv.org/abs/2103.14749) and demo (https://labelerrors.com/).

Since then, I’ve been interested in building tools to automate this sort of analysis. We’ve finally gotten to the point where a web app can do automatically in a couple of hours what I spent months doing in Jupyter notebooks back in 2019—2020. It was really neat to see the software we built automatically produce the same figures and tables that are in our papers.

The blog post shared here is results-focused, talking about some of the data and dataset-level issues that a tool using data-centric AI algorithms can automatically find in ImageNet, which we used as a case study. Happy to answer any questions about the post or data-centric AI in general here!

P.S. all of our core algorithms are open-source, in case any of you are interested in checking out the code: https://github.com/cleanlab/cleanlab

Cleanlab Studio offers a user-friendly interface that allows you to visualize and review the identified issues in your dataset. You can easily explore the detected errors and make corrections with confidence. It's a hassle-free solution that can save you valuable time and improve your overall e-commerce operations. If you'd like more details you can check this article out.

I thought this community might find it very useful that Databricks has partnered with Cleanlab to bring automated data correction and ML model improvement for both structured and unstructured datasets to all Databricks users.

I just published a paper detailing this non-IID check and open-sourced its code in the cleanlab package — just one line of code will check for this and many other types of issues in your dataset.

I recently published a blog introducing Datalab and an open-source Python implementation that is easy-to-use for all data types (image, text, tabular, audio, etc). For data scientists, I’ve made a quick Jupyter tutorial to run Datalab on your own data.