# A clustering-based approach to create deep learning datasets in a day

DevFeed: [A clustering-based approach to create deep learning datasets in a day](<https://devfeed.tech/articles/dataset-in-a-day-22600.md>)

Original publisher: [Read original article](<https://medium.com/bumble-tech/dataset-in-a-day-7f369de3b178?source=rss----6353b5325b1a---4>)

Author: Roland Meertens

Published: 2023-11-28T17:33:30Z

Content type: article

Language: en

Sources: [Bumble Tech](<https://devfeed.tech/sources/bumble-tech.md>)

Topics: [dataset](<https://devfeed.tech/topics/dataset.md>), [Computer vision](<https://devfeed.tech/topics/computer-vision.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Inference](<https://devfeed.tech/topics/inference.md>)

Tags: [clustering](<https://devfeed.tech/tags/clustering.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference](<https://devfeed.tech/tags/inference.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [models](<https://devfeed.tech/tags/models.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [zero-shot](<https://devfeed.tech/tags/zero-shot.md>)

## AI overview

The article discusses the cost and time involved in creating labeled computer vision datasets. It considers zero-shot learning and foundational models such as GPT-3 and CLIP for data retrieval, while noting that some use cases still require fine-tuning on task-specific data.

## Source excerpt

A clustering-based approach to create deep learning datasets in a day Introduction Understanding what's happening in an image is both an important task, as well as a costly one. In the last few years, the field of computer vision has greatly accelerated due to the advances in neural networks. At Bumble Inc., we see potential value in computer vision for a variety of use cases, such as improving the safety of our platform and providing our members with a better user experience. The most common way to train these neural networks is by showing it many images with the corresponding label. Unfortunately, this can be a costly task. Not only does one need to build and train the model, one also wants to do hyperparameter search over multiple configurations of possible networks, and -- of course -- one needs to find or build a dataset suitable for the task at hand. Building the dataset is both the most important task, as well as a very time consuming one. Gathering data, setting up labelling requirements, and of course the labelling itself all take a lot of time and money. This normally leads to trade-offs, by choosing either to build only a small dataset, or by trying to fit existing datasets into your specific use-case. One alternative is of course to not build a dataset at all, to instead use zero-shot learning for your use case. I argued in the past that this is unreasonably effective, and allows you to test your use-case before even training a model. When using zero-shot learning one predicts labels without explicitly training on the classes you are trying to learn. One example of this can be achieved by using the CLIP model, which is trained to have a strong association between text and images. By looking at the distance between the description of your class and the image you can run inference without training anything. However, there are some use cases where we need the strongest possible model by fine-tuning it to our specific data. Using foundational models for data s