CLIP
fastdup
CLIP | fastdup | |
---|---|---|
104 | 18 | |
22,605 | 1,421 | |
4.2% | 2.3% | |
1.0 | 9.4 | |
7 days ago | about 2 months ago | |
Jupyter Notebook | Python | |
MIT License | GNU General Public License v3.0 or later |
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.
CLIP
-
Anomaly Detection with FiftyOne and Anomalib
pip install -U huggingface_hub umap-learn git+https://github.com/openai/CLIP.git
-
How to Cluster Images
We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:
-
Show HN: Memories, FOSS Google Photos alternative built for high performance
Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.
-
Zero-Shot Prediction Plugin for FiftyOne
In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
-
A History of CLIP Model Training Data Advances
(Github Repo | Most Popular Model | Paper | Project Page)
-
NLP Algorithms for Clustering AI Content Search Keywords
the first thing that comes to mind is CLIP: https://github.com/openai/CLIP
-
How to Build a Semantic Search Engine for Emojis
Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.
-
COMFYUI SDXL WORKFLOW INBOUND! Q&A NOW OPEN! (WIP EARLY ACCESS WORKFLOW INCLUDED!)
in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
-
Stability Matrix v1.1.0 - Portable mode, Automatic updates, Revamped console, and more
Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary
-
[D] LLM or model that does image -> prompt?
CLIP might work for your needs.
fastdup
-
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
-
[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.
-
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.
-
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.
-
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
-
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
What are some alternatives?
open_clip - An open source implementation of CLIP.
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
computervision-recipes - Best Practices, code samples, and documentation for Computer Vision.
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
disco-diffusion
dhash - Python library to calculate the difference hash (perceptual hash) for a given image, useful for detecting duplicates
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
CVPR2024-Papers-with-Code - CVPR 2024 论文和开源项目合集
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
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