anomalib

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference. (by openvinotoolkit)

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better anomalib alternative or higher similarity.

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anomalib reviews and mentions

Posts with mentions or reviews of anomalib. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-09.
  • Computer Vision Meetup: Anomaly Detection with Anomalib and FiftyOne
    1 project | dev.to | 10 May 2024
    In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.
  • Recapping the AI, Machine Learning and Data Science Meetup — May 8, 2024
    3 projects | dev.to | 9 May 2024
    Anomalib GitHub
  • Anomaly Detection with FiftyOne and Anomalib
    4 projects | dev.to | 6 May 2024
    In this post, you'll learn how to perform anomaly detection on visual data using FiftyOne and Anomalib from the OpenVINO toolkit. For demonstration, we'll use the MVTec AD dataset, which contains images of various objects with anomalies like scratches, dents, and holes.
  • May 8, 2024 AI, Machine Learning and Computer Vision Meetup
    2 projects | dev.to | 1 May 2024
    This talk highlights the role of Anomalib, an open-source deep learning framework, in advancing anomaly detection within AI systems, particularly showcased at the upcoming CVPR Visual Anomaly and Novelty Detection (VAND) workshop. Anomalib integrates advanced algorithms and tools to facilitate both academic research and practical applications in sectors like manufacturing, healthcare, and security. It features capabilities such as experiment tracking, model optimization, and scalable deployment solutions. Additionally, the discussion will include Anomalib’s participation in the VAND challenge, focusing on robust real-world applications and few-shot learning for anomaly detection.
  • Anomalib: Anomaly detection library comprising cutting-edge algorithms
    1 project | news.ycombinator.com | 24 Apr 2024
  • Exploring Open-Source Alternatives to Landing AI for Robust MLOps
    18 projects | dev.to | 13 Dec 2023
    Then, when it comes to semi-supervised learning for anomaly detection, I had positive experiences with Anomalib which offers a robust library dedicated to deep learning anomaly detection algorithms. They implemented the latest models with PyTorch and offer tools to benchmark their performance.
  • Defect Detection using Computer Vision
    1 project | /r/computervision | 5 Dec 2023
  • From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images
    4 projects | dev.to | 15 Oct 2023
    Anomalib is an open-source library for unsupervised anomaly detection in images. It offers a collection of state-of-the-art models that can be trained on your specific images.
  • FLaNK Stack Weekly for 07August2023
    27 projects | dev.to | 7 Aug 2023
  • Powering Anomaly Detection for Industry 4.0
    2 projects | dev.to | 24 Jul 2023
    Anomalib is an open-source deep learning library developed by Intel that makes it easy to benchmark different anomaly detection algorithms on both public and custom datasets, all by simply modifying a config file. As the largest public collection of anomaly detection algorithms and datasets, it has a strong focus on image-based anomaly detection. It’s a comprehensive, end-to-end solution that includes cutting-edge algorithms, relevant evaluation methods, prediction visualizations, hyperparameter optimization, and inference deployment code with Intel’s OpenVINO Toolkit.
  • A note from our sponsor - Nutrient
    nutrient.io | 19 Feb 2025
    Bad PDFs = bad UX. Slow load times, broken annotations, clunky UX frustrates users. Nutrient’s PDF SDKs gives seamless document experiences, fast rendering, annotations, real-time collaboration, 100+ features. Used by 10K+ devs, serving ~half a billion users worldwide. Explore the SDK for free. Learn more →

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8 days ago

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