anomalib VS label-studio

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.

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.

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.

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.

try https://github.com/openvinotoolkit/anomalib it's primarily aimed at vision applications but might provide some inspiration

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.

If instead you have a cohort on hand — -i.e., you do not want to send your data to a third party for any reason, or perhaps you have energetic undergrads — -then you could alternatively consider local, open-source annotation such as CVAT and Label Studio. Finally, nowadays, you might instead work with Large Multimodal Models to have them annotate your data; more on this awkward angle later.

14. LabelStudio by Human Signal | Github | tutorial

For instance, the COCO Annotator is a web-based image annotation tool tailored for the COCO dataset format, allowing collaborative labeling with features like attribute tagging and automatic segmentation. Similarly, Label Studio offers an easy-to-use interface for bounding box object labeling in images.

Huh?

I wrote my own system for classifying a stream of texts in Python, I might Open Source it one of these days but I have to get it to the point where it is modular enough that I can customize it to do the particular things I want without subjecting people to my whims... I use it every day and I'm not afraid to demo it because it is rock solid.

My understanding is that my system would not be hard to adapt to work on images for certain kinds of tasks.

Pytorch is open source, Huggingface is open source. CUDA isn't. This is

https://labelstud.io/

and for annotating text spans there are so many open source tools

https://github.com/doccano/doccano

I worked for a company a few years back that built annotation tools for projects we sold to customers but never quite got to a polished general purpose annotator. Today there are an overwhelming number of companies in this space and products I never heard of, many of which are cloud based or paid. Looks like a gold rush to me.

LabelStudio is free for single use. https://labelstud.io/

LabelStudio is pretty nice, and free & open source, but I have yet to try out their ML integration with a YOLO object detection model.

Label Studio is an open source data labeling tool that includes annotation functionality. It provides a simple user interface (UI) that lets you label various data types, including text, audio, time series data, videos, and images, and export the information to various model formats.