anomalib
OpenCV
anomalib | OpenCV | |
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14 | 196 | |
3,154 | 75,692 | |
3.5% | 1.0% | |
9.3 | 9.9 | |
3 days ago | 2 days ago | |
Python | C++ | |
Apache License 2.0 | Apache License 2.0 |
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anomalib
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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
OpenCV
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การจำแนกสายพันธุ์มะม่วง โดยใช้ Visual Geometry Group 16 (VGG16) ใน Python
Referenceshttps https://www.kaggle.com/datasets/riyaelizashaju/skin-disease-image-dataset-balanced?fbclid=IwAR3wbTp8l5yo_5fx6HAX8Vd2-9cca3khAc8EiBGFObaALfdVid29IuB_rYE https://keras.io/api/applications/vgg/ https://www.tensorflow.org/tutorials/images/cnn?hl=th https://opencv.org/
- Opencv-Python adds support for Pathlike objects
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
- OpenCV calls for help
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Image segmentation in huggingface
You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)
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Looking for a Windows auto-clicker with conditions
You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/).
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NodeJS: Blurring Human Faces in Photos
The OpenCV4NodeJs A.I. library provides an interface for calling OpenCV routines in NodeJS.
- NodeJS - Ofuscando rostos humanos em fotos
- SIMD Everywhere Optimization from ARM Neon to RISC-V Vector Extensions
- VidCutter: A program for lossless video cutting
What are some alternatives?
anomaly-detection-resources - Anomaly detection related books, papers, videos, and toolboxes
libvips - A fast image processing library with low memory needs.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
VTK - Mirror of Visualization Toolkit repository
ncappzoo - Contains examples for the Movidius Neural Compute Stick.
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
pycaret - An open-source, low-code machine learning library in Python
CImg - The CImg Library is a small and open-source C++ toolkit for image processing
fiftyone - The open-source tool for building high-quality datasets and computer vision models
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
gorilla-cli - LLMs for your CLI
Boost.GIL - Boost.GIL - Generic Image Library | Requires C++14 since Boost 1.80