imagededup
CLIP
imagededup | CLIP | |
---|---|---|
7 | 103 | |
4,951 | 22,209 | |
0.6% | 2.5% | |
1.5 | 1.2 | |
7 days ago | 22 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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imagededup
- Reverse Image Search Local Files? (NOT A DUPLICATE FINDER)
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Help checking for duplicate images with large number of images
imagededup
- Is there a software, that allows me to find all duplicates of ONE picture?
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fastest way to go about calculating hamming distance?
To generate the hashes I was just going to use https://github.com/idealo/imagededup as it has been effective in my testing. The issue is the scalability issue. That code even is able to find similar images, using distance hamming. But they didn't care about scalability, and that is now my biggest bottleneck.
- Find visual similar photos
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PowerShell skript for finding duplicate pictures
Which is why, it's much better to use a dedicated tool that can visually compare photos (using a neural net/machine learning model) to identify duplicate images. If you want a command-line tool for scripting purposes then check out imgdup2go or imagededup.
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How to remove duplicate images from your dataset (Also CIFAR-100 has issues)
I had used phash to find the duplicate images. The hashing algorithm is ingenious...And it was able to find duplicates (and thus remove) efficiently imagededub has perceptual hashing.
CLIP
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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:
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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.
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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:
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A History of CLIP Model Training Data Advances
(Github Repo | Most Popular Model | Paper | Project Page)
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NLP Algorithms for Clustering AI Content Search Keywords
the first thing that comes to mind is CLIP: https://github.com/openai/CLIP
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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.
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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).
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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
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[D] LLM or model that does image -> prompt?
CLIP might work for your needs.
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Where can this be used? I have seen some tutorials to run deepfloyd on Google colab. Any way it can be done on local?
pip install deepfloyd_if==1.0.2rc0 pip install xformers==0.0.16 pip install git+https://github.com/openai/CLIP.git --no-deps pip install huggingface_hub --upgrade
What are some alternatives?
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
open_clip - An open source implementation of CLIP.
floc-simhash - A fast python implementation of the SimHash algorithm.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
alipay - An Unofficial Alipay API for Python
disco-diffusion
DeepCreamPy - Decensoring Hentai with Deep Neural Networks
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
Duplicate-Image-Finder - difPy - Python package for finding duplicate or similar images within folders
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation