CLIP
fiftyone
CLIP | fiftyone | |
---|---|---|
104 | 21 | |
22,605 | 6,781 | |
4.2% | 3.1% | |
1.0 | 10.0 | |
7 days ago | 4 days ago | |
Jupyter Notebook | Python | |
MIT License | Apache License 2.0 |
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CLIP
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Anomaly Detection with FiftyOne and Anomalib
pip install -U huggingface_hub umap-learn git+https://github.com/openai/CLIP.git
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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.
fiftyone
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Anomaly Detection with FiftyOne and Anomalib
pip install -U git+https://github.com/voxel51/fiftyone.git
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May 8, 2024 AI, Machine Learning and Computer Vision Meetup
In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.
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Voxel51 Is Hiring AI Researchers and Scientists — What the New Open Science Positions Mean
My experience has been much like this. For twenty years, I’ve emphasized scientific and engineering discovery in my work as an academic researcher, publishing these findings at the top conferences in computer vision, AI, and related fields. Yet, at my company, we focus on infrastructure that enables others to unlock scientific discovery. We have built a software framework that enables its users to do better work when training models and curating datasets with large unstructured, visual data — it’s kind of like a PyTorch++ or a Snowflake for unstructured data. This software stack, called FiftyOne in its single-user open source incarnation and FiftyOne Teams in its collaborative enterprise version, has garnered millions of installations and a vibrant user community.
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How to Estimate Depth from a Single Image
We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.
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How to Cluster Images
With all that background out of the way, let’s turn theory into practice and learn how to use clustering to structure our unstructured data. We’ll be leveraging two open-source machine learning libraries: scikit-learn, which comes pre-packaged with implementations of most common clustering algorithms, and fiftyone, which streamlines the management and visualization of unstructured data:
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Efficiently Managing and Querying Visual Data With MongoDB Atlas Vector Search and FiftyOne
FiftyOne is the leading open-source toolkit for the curation and visualization of unstructured data, built on top of MongoDB. It leverages the non-relational nature of MongoDB to provide an intuitive interface for working with datasets consisting of images, videos, point clouds, PDFs, and more.
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FiftyOne Computer Vision Tips and Tricks - March 15, 2024
Welcome to our weekly FiftyOne tips and tricks blog where we recap interesting questions and answers that have recently popped up on Slack, GitHub, Stack Overflow, and Reddit.
- FLaNK AI for 11 March 2024
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How to Build a Semantic Search Engine for Emojis
If you want to perform emoji searches locally with the same visual interface, you can do so with the Emoji Search plugin for FiftyOne.
- FLaNK Stack Weekly for 07August2023
What are some alternatives?
open_clip - An open source implementation of CLIP.
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
ZnTrack - Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
disco-diffusion
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
streamlit - Streamlit — A faster way to build and share data apps.
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.