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Blog Posts
Top 5 Computer Vision Use Cases for Consumers
In this blog, we discuss some of the most innovative B2C computer vision use cases we have come across after speaking with more than 100 companies last year.
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Self-Supervised Models are More Robust and Fair
A recent paper from Meta AI Research shows that their new 10 billion parameter model trained using self-supervised learning breaks new ground in robustness and fairness.
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Bias in Machine Learning
This blog post will give a high-level overview on the topic of bias in machine learning, a significant issue that can often be traced back to the data used to train an algorithm. I will discuss the different types of bias in machine learning, and how to identify and analyze it using different tools.
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Lightly's Data Curation Approach
This overview is intended to help everyone understand how Lightly works behind the scenes and how it can be used for your data curation workflow.
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Train Test Split in Deep Learning
One of the golden rules in machine learning is to split your dataset into train, validation, and test set. Learn how to bypass the most common caveats!
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The Best Data Curation Tools for Computer Vision in 2022
Integrating a quality data curation tool into your ML pipeline will have a direct impact on the quality and performance of your model. With so many solutions on the market, it can be difficult to get a clear understanding of which to choose. In this article, we describe the top data curation tools of 2022.
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What is data redundancy in computer vision?
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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Why are data pipelines important?
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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How should I build my data pipeline for computer vision?
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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What is a data pipeline in computer vision?
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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Data Selection for Computer Vision in 5 Steps
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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Predicting Rain from Satellite Images (Part 2)
Can a neural network predict rain from satellite images? Part 2: Method and results
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Predicting Rain from Satellite Images (Part 1)
Can a neural network predict rain from satellite images? Part 1: Data collection and analysis
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5 Answers to Data Problems in Computer Vision
This article presents 5 ways Lightly helps tackling data problems in the computer vision machine learning field.
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Active Learning with Nvidia TLT
How to go from a quick prototype to a production ready object detection system using active learning and Nvidia TLT.
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Active Learning using Detectron2
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 x Lightly
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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Data Preparation Tools for Computer Vision 2021
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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Sustainable AI and the New Data Pipeline
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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The Advantage of Self-Supervised Learning
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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Embedded COVID mask detection on an Arm Cortex-M7 processor using PyTorch
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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Few-Shot Learning with fast.ai
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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How to filter redundant data
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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Which Optimizer should I use for my ML Project?
This article provides a summary of popular optimizers used in computer vision, natural language processing, and machine learning in general.
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AI Strategy for Business Leaders
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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Rotoscoping: Hollywood’s video data segmentation?
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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Introducing What To Label - data preparation
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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Data Labeling: AI’s Human Bottleneck
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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Data You Don‘t Need: Removing Redundancy
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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What Is Active Learning in Machine Learning?
Understand what active learning is in machine learning, it's inner workings, how it can be useful using examples and use cases.
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What is Data Curation and Why is it Important?
In order to train good machine learning models on data , the data must be curated. In this post, we'll talk about what data curation is and more.
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How to Remove Bias from Data for Machine Learning
In this post, we'll cover bias in data and ways to remove bias, specifically how to reduce certain kinds of bias from machine learning data.
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