fastdup
pyod
| fastdup | pyod | |
|---|---|---|
| 18 | 7 | |
| 1,403 | 7,962 | |
| 1.0% | - | |
| 9.4 | 7.5 | |
| 28 days ago | 1 day ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | BSD 2-clause "Simplified" License |
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fastdup
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Visualize your dataset using DINOv2 embedding
Visualizing your dataset (especially large ones) in a low-dimensional embedding space can tell you a lot about the patterns and clusters in your dataset.
We recently release a notebook showing how you can visualize your dataset using DINOv2 models by running it on your CPU.
Yes! No GPUs needed.
We used it to find clusters of similar images, duplicates, and outliers in a subset of the LAION dataset
Try it on your own dataset:
Colab notebook - https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/dinov2_notebook.ipynb
GitHub repo - https://github.com/visual-layer/fastdup
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[R][P] How to extract feature vectors of large datasets using DINOv2 on CPU
Run 1M images from the LAION dataset through the DINOv2 model and cluster the images using a free tool - fastdup.
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Computer Vision pre-trained model for finding how similar two photos of a room are
Another option could be fastdup (https://github.com/visual-layer/fastdup) which is probably most helpful for analysis type objectives.
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Find image duplicates and outliers – A free, scalable, efficient tool
I recently stumbled upon fastdup a tool that lets you gain insights from a large image/video collection.
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How can we match images in our database?
There is this fastdup framework which supposedly allows you to find duplicates and similar images. i haven't used it though
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Measure Images Similarity
I came across fastdup recently https://github.com/visual-layer/fastdup
- Dedup-ing LAION (60M duplicates) and ImageNet (1.2M duplicates) with fastdup
- [R] Dedup-ing LAION (60M duplicates) and ImageNet (1.2M duplicates) with fastdup
pyod
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A Comprehensive Guide for Building Rag-Based LLM Applications
This is a feature in many commercial products already, as well as open source libraries like PyOD. https://github.com/yzhao062/pyod
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Analyze defects and errors in the created images
PyOD
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Multivariate Outlier Detection in Python
Check out the algorithms and documentation in this toolkit. It’ll give you a list of methods to read up on to understand their mechanisms. https://github.com/yzhao062/pyod
- Pyod – A Comprehensive and Scalable Python Library for Outlier Detection
- Predictive Maintenance and Anomaly Detection Resources
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[D] Unsupervised Outlier Detection - Advise Requested
The source code and documentaion of PyOD is the best survey about OOD. Besides, the normalized flow and VQVAE are also feasible.
- PyOD: ~50 anomaly detection algorithms in one framework.
What are some alternatives?
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
tods - TODS: An Automated Time-series Outlier Detection System
computervision-recipes - Best Practices, code samples, and documentation for Computer Vision.
isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
dhash - Python library to calculate the difference hash (perceptual hash) for a given image, useful for detecting duplicates
alibi-detect - Algorithms for outlier, adversarial and drift detection
CVPR2024-Papers-with-Code - CVPR 2024 论文和开源项目合集
pycaret - An open-source, low-code machine learning library in Python
albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
plakakia - Python image tiling library for image processing, object detection, etc.
stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis