What is redundant data and why should you avoid it? This article will tackle these questions in the context of computer vision by providing concrete examples. Data redundancy is shown to have negative repercussions on model accuracy and to be wasteful of resources.

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Data pipelines are important because, without a professional setup, errors can occur which jeopardize dataset quality. At the same time, a lot of resources are wasted on manual work due to the inefficient setup. In some of the worst cases, we have seen millions of dollars of resources wasted on bad data which naturally resulted in disappointing outcomes from the whole machine learning project. 

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A 3-step guide for building data pipelines for computer vision: Setting up the data pipeline aims to automate data streams and transfers, data selection, and dataset management (read more about what a data pipeline is here). Thus, automated processes should be the primary focus when building a data pipeline. This article will elaborate on how to build a data pipeline for computer vision in 3 steps.

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A data pipeline in computer vision is the path the data flows through. From data collection to storage, being used for model training, and deployment. Ideally, it is a connected technical set-up where data storage is linked to various data preparation and MLops tools, which in turn are connected through an API to the machine learning model and the deployed product.

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This blog post suggests five chronological steps to select data for computer vision tasks: (1) understanding collected data, (2) defining requirements for the training dataset, (3) sampling the best subset with diversity-based sampling and self-supervised learning, (4) improving the model iteratively with uncertainty-based sampling and active learning, and finally, (5) automating the data pipeline by continuously adding the right samples to the data repository.‍

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Can a neural network predict rain from satellite images? Part 2: Method and results

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Can a neural network predict rain from satellite images? Part 1: Data collection and analysis

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This article presents 5 ways Lightly helps tackling data problems in the computer vision machine learning field.

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How to go from a quick prototype to a production ready object detection system using active learning and Nvidia TLT.

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Tired of labeling all your data? Learn more about how model predictions and embeddings can help you select the right data.

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Startup@HSG featured an interview with Lightly co-founder Matthias in their monthly newsletter. It gives you a peek behind the curtain; how Lightly came to be and what's next for us.

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This article provides a data preparation tool landscape for computer vision. The intention is to give an overview of the available solutions which machine learning engineers can use to build better models.

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Deep learning's requirement of Big and Smart Data is currently met by labor-intensive processes of data labeling and cleaning. Self-supervised learning challenges this paradigm by enabling a more sustainable data pipeline.

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‍A few personal thoughts on why self-supervised learning will have a strong impact on AI. From recent NLP to computer vision papers.

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How we built a visual COVID-19 mask quality inspection prototype running on-device on an OpenMV-H7 board and the challenges on the way.

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‍In few-shot learning, we train a model using only a few labeled examples. Learn how to train your classifier using transfer learning and a novel framework for sample selection.

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Many interesting Deep learning applications rely on the use of complex architectures fueled by large datasets. However, when doing so, a new challenge surfaces: data redundancy.

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This article provides a summary of popular optimizers used in computer vision, natural language processing, and machine learning in general.

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In today’s globalized world, competition is becoming more and more intense. Products are getting better and cheaper. Can this race be won? How do you protect yourself from being disrupted by new, innovative products?

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In Hollywood, video data segmentation has been done for decades. Simple tricks such as color keying with green screens can reduce work significantly. In late 2018 we worked on a video segmentation toolbox.

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Are you curious about research areas such as active, self-supervised, and semi-supervised learning and how we can optimize datasets rather than optimizing deep learning models? You’re in good company, and this blog post will tell you all about it!

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Customers are increasingly demanding smart products such as autonomous cars or home assistants. This leads to the expected growth of the AI market to over $100Bn by 2025 (image below). But what does it take to make a product smart?

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In ML there is the saying garbage in, garbage out. But what does it really mean to have good or bad data? In this post, we will explore data redundancies in the training set of fashion-MNIST and how it affects test set accuracy.

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