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)
anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes (by yzhao062)
anomalib | anomaly-detection-resources | |
---|---|---|
14 | 98 | |
3,154 | 7,887 | |
3.5% | - | |
9.3 | 4.6 | |
3 days ago | 11 days ago | |
Python | Python | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
anomalib
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 2024-05-01.
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May 8, 2024 AI, Machine Learning and Computer Vision Meetup
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
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
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
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From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images
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
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Powering Anomaly Detection for Industry 4.0
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.
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Early anomaly detection / Failure prediction on time series
try https://github.com/openvinotoolkit/anomalib it's primarily aimed at vision applications but might provide some inspiration
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Anomaly detection in images using PatchCore
Anomaly detection typically refers to the task of finding unusual or rare items that deviate significantly from what is considered to be the "normal" majority. In this blogpost, we look at image anomalies using PatchCore. Next to indicating which images are anomalous, PatchCore also identifies the most anomalous pixel regions within each image. One big advantage of PatchCore is that it only requires normal images for training, making it attractive for many use cases where abnormal images are rare or expensive to acquire. In some cases, we don't even know all the unusual patterns that we might encounter and training a supervised model is not an option. One example use case is the detection of defects in industrial manufacturing, where most defects are rare by definition as production lines are optimised to produce as few of them as possible. Recent approaches have made significant progress on anomaly detection in images, as demonstrated on the MVTec industrial benchmark dataset. PatchCore, presented at CVPR 2022, is one of the frontrunners in this field. In this blog post we first dive into the inner workings of PatchCore. Next, we apply it to an example in medical imaging to gauge its applicability outside of industrial examples. We use the anomalib library, which was developed by Intel and offers ready-to-use implementations of many recent image anomaly detection methods.
- Defect Detection using RPI
anomaly-detection-resources
Posts with mentions or reviews of anomaly-detection-resources.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-20.
- anomaly-detection-resources: NEW Extended Research - star count:7507.0
- anomaly-detection-resources: NEW Extended Research - star count:7323.0
- anomaly-detection-resources: NEW Extended Research - star count:7109.0
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Time-based splitting performing significantly worse than random splitting
https://github.com/yzhao062/anomaly-detection-resources https://search.brave.com/search?q=imbalanced+dataset+machine+learning+github&source=desktop
What are some alternatives?
When comparing anomalib and anomaly-detection-resources you can also consider the following projects:
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)
ncappzoo - Contains examples for the Movidius Neural Compute Stick.
loglizer - A machine learning toolkit for log-based anomaly detection [ISSRE'16]
pycaret - An open-source, low-code machine learning library in Python
fiftyone - The open-source tool for building high-quality datasets and computer vision models
gorilla-cli - LLMs for your CLI
DGFraud - A Deep Graph-based Toolbox for Fraud Detection
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
UGFraud - An Unsupervised Graph-based Toolbox for Fraud Detection
anomalib vs pyod
anomaly-detection-resources vs pygod
anomalib vs ncappzoo
anomaly-detection-resources vs loglizer
anomalib vs pycaret
anomaly-detection-resources vs pycaret
anomalib vs fiftyone
anomaly-detection-resources vs pyod
anomalib vs gorilla-cli
anomaly-detection-resources vs DGFraud
anomalib vs openvino
anomaly-detection-resources vs UGFraud