fastdup
snip-dedup
| fastdup | snip-dedup | |
|---|---|---|
| 18 | 1 | |
| 1,408 | 95 | |
| 1.0% | - | |
| 9.4 | 5.7 | |
| 29 days ago | 3 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT 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
snip-dedup
What are some alternatives?
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
computervision-recipes - Best Practices, code samples, and documentation for Computer Vision.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
dhash - Python library to calculate the difference hash (perceptual hash) for a given image, useful for detecting duplicates
CVPR2024-Papers-with-Code - CVPR 2024 论文和开源项目合集
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
plakakia - Python image tiling library for image processing, object detection, etc.
visionner - Visionner turn raw image data into numpy array, more suitable for deep learning task
flockfysh - A simple data vending machine that pops more out that what comes in. Use flockfysh to seamlessly pool existing datasets with quality web-scraped data to get top notch datasets.
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
omni3d - Code release for "Omni3D A Large Benchmark and Model for 3D Object Detection in the Wild"
research-papers