CLIP
fastbook
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CLIP | fastbook | |
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103 | 23 | |
22,051 | 20,711 | |
5.6% | 1.8% | |
1.2 | 2.6 | |
15 days ago | 13 days ago | |
Jupyter Notebook | Jupyter Notebook | |
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
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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
fastbook
- The fastai book, published as Jupyter Notebooks
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fast.ai Book in Rust - Chapter 2 - Part 1
This chapter focuses on defining the DataLoader classes and a Bing Image Search downloader that is provided with the fastai library. We're not going to implement a Bing downloader. That is too much work for something that could be a crate on its own. Please feel free to write such a crate, though, the world could use one.
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Fastai Chapter 4 - The important parts, Part 2: Building a regression model
The book is available online here The course is accessible here
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Need help trying to run Fastai notebooks on kaggle.
Fastai Lesson 2 notebook
- Fast.ai's Practical Deep Learning for Coders Has Been Updated
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How can i as 15 years old start learning machine learning, i watched some python courses on youtube but it covered the basics and I want to go more in depth. Are there any books, online courses, etc.. I cant really pay for anything so no paid courses. Thank you
I recently read the FastAI book from O'Reilly, which is also published as a series of notebooks on GitHub here. I personally liked it because it shows how to obtain a working model trained with modern techniques without delving too much in the low-level details.
- [D] Recommendation of books to achieve a deeper knowledge of the field
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I recently got a bit of money from my grandparents to get myself a present and I wanted to get a good Python book. Which book would you recommend?
I recommend fastai-fastbook. I just started myself though it’s a coupled with tools and a way of working that may help you including being and to create and publish python packages from a jupyter notebook using nbdev
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“Perceptron” paved the way for AI 60 years too soon (2019)
The fastai book actually makes a nice comparison between the systems described in PDP and modern deep learning.
> In fact, the approach laid out in PDP is very similar to the approach used in today's neural networks.
From: https://github.com/fastai/fastbook/blob/master/01_intro.ipyn...
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Starting a career as a Python developer
I’m a fan of fast book by fastai.
What are some alternatives?
open_clip - An open source implementation of CLIP.
fastai - The fastai deep learning library
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
Franklin.jl - (yet another) static site generator. Simple, customisable, fast, maths with KaTeX, code evaluation, optional pre-rendering, in Julia.
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
car-damage-detection - Detectron2 for car damage detection using custom dataset
disco-diffusion
Hands-On-Deep-Learning-Algorithms-with-Python - Hands-On Deep Learning Algorithms with Python, By Packt
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
articulated-animation - Code for Motion Representations for Articulated Animation paper
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
models - Models and examples built with TensorFlow