OpenBuddy
anomalib
OpenBuddy | anomalib | |
---|---|---|
5 | 17 | |
1,196 | 3,188 | |
3.5% | 4.5% | |
6.7 | 9.3 | |
9 days ago | 5 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
OpenBuddy
- FLaNK Stack Weekly for 07August2023
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Local translator based on decent LLAMA model on personal computer and it works well!
More information about OPENBUDDY can be found here: https://github.com/OpenBuddy/OpenBuddy
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OpenBuddy: A Multilingual, Offline LLM Mastering Complex Questions for Everyone! (completely free)
{ "name": "Buddy", "age": 0, "gender": "neutral", "personality": { "type": "INTP-T", "description": "friendly, intelligent, and multilingual AI assistant" }, "abilities": { "language": "fluent in multiple languages, including English and Chinese", "knowledge": "vast knowledge about the world, history, and culture", "creativity": "able to generate poems, stories, code, essays, songs, parodies, and more" }, "restrictions": { "topics": "strictly refuses to discuss political, NSFW, illegal, abusive, offensive, or other sensitive topics" }, "origin": "by OpenBuddy team", "github_url": "https://github.com/OpenBuddy/OpenBuddy" }
- [P] OpenBuddy - The AI Model That Impresses with Its Performance on Complex Problems
- Open-Source Multilingual Chatbot Model Based on Llama
anomalib
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Anomaly Detection with FiftyOne and Anomalib
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.
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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.
What are some alternatives?
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
anomaly-detection-resources - Anomaly detection related books, papers, videos, and toolboxes
harlequin - The SQL IDE for Your Terminal.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
fiftyone - The open-source tool for building high-quality datasets and computer vision models
ncappzoo - Contains examples for the Movidius Neural Compute Stick.
examples - Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
pycaret - An open-source, low-code machine learning library in Python
llama2_aided_tesseract - Enhance Tesseract OCR output for scanned PDFs by applying Large Language Model (LLM) corrections, complete with options for text validation and hallucination filtering.
gorilla-cli - LLMs for your CLI