clip-retrieval
clip-as-service
clip-retrieval | clip-as-service | |
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11 | 15 | |
2,139 | 12,202 | |
- | 0.4% | |
7.7 | 5.2 | |
23 days ago | 4 months ago | |
Jupyter Notebook | Python | |
MIT License | GNU General Public License v3.0 or later |
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clip-retrieval
- FLaNK AI for 11 March 2024
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[D] data for handwriting recognition
The tool clip-retreival lets you filter those 400 million images to whatever subsets you're interested in --- for example, 10,000 images of (mostly) handwriting.
- Stable Attribution
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Same.energy: Image Search by Similarity
Hehe, well you know, PR welcome, the front end is 500 lines https://github.com/rom1504/clip-retrieval/blob/main/front/sr...
Other people have done a few alternate front ends already
This one is meant to be functional, but could sure be made prettier
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Is there a way to use clip or blip to search a massive collection of images for specific things within the picture?
This might work: https://github.com/rom1504/clip-retrieval .
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Ai art
HaveIBeenTrained uses clip retrieval to search the Laion-5B and Laion-400M image datasets. These are currently the largest public text-to-image datsets, and they are used to train models like Stable Diffusion, Imagen, among many others.
- Image Similarity Score using transfer learning
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Exploring 12M of the 2.3B Images Used to Train Stable Diffusion
Done https://github.com/rom1504/clip-retrieval/commit/53e3383f58b...
Using clip for searching is better than direct text indexing for a variety of reasons but here for example because it matches better what stable diffusion sees
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Semantic and Similarity Image Search Engine
Based on OpenAI's CLIP and the clip-retrieval library (https://github.com/rom1504/clip-retrieval), I've built an end-to-end demo for a semantic and similarity image search engine. It's incredibly powerful for finding similar images amongst large image datasets, or just submitting text/natural language queries and finding the most relevant images in your dataset. Really useful tool for introspection into large datasets before annotation or ML work begins. This could potentially be used to filter or downsize your datasets by several orders of magnitude and make annotation and ML work easier and less costly.
Checkout the demo here:
http://ec2-52-39-251-116.us-west-2.compute.amazonaws.com/
And you can checkout our website or email me for updates and email list, etc.:
https://machineperception.co
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What every software engineer should know about search
Assuming you have an NVIDIA GPU, you can build a semantic search engine by indexing CLIP embeds (image or text).
https://github.com/rom1504/clip-retrieval
clip-as-service
- Search for anything ==> Immich fails to download textual.onnx
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I'm going insane trying to train large datasets for poses, any input would be greatly appreciated I've been stuck for days
I think training models with limited images can lead to overfitting, so I think you can try using a set of images with different poses. You might also want to try flipping or to help out the model so it gets to do different psoes. You might also want CLIP-as-a-service, but just know that pre-trained models isn't always be the best solution. My .02c
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[D]Want to Search Inside Videos Like a Pro?
Imagine an AI-powered grep command, one that could process a film and find segments matching a text. With CLIP-as-service, you can do that. Here is the repo link, https://github.com/jina-ai/clip-as-service.
- Image Similarity Score using transfer learning
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Best models for sentence similarity with good benefit-cost ratio?
you could try Jina.ai's CLIP-as-a-Service: https://github.com/jina-ai/clip-as-service
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Google launched multisearch last week, here's how you can create your own multisearch
Multisearch allows people to search with both text and images. With Open-Source project CLIP-as-service, you can use CLIP (a deep learning model by OpenAI) to do the same. Ask me if you have any questions?
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Natural text to image search(without captions), using CLIP model. Notebook in comment.
Are you scraping these images or using any dataset? Do share the link, would love to play around with it. Would love to hear your feedback for clip-as-service (what I use in my example)?
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Open-Source python package to find relevant images for a sentence
Built CLIP-as-service, an open-source library to create embeddings of images and text using CLIP. These embeddings can be used to find the relevant images for any sentence. Note: you don't need to caption the images for this to work, and it is not just limited to objects in the image but an overall understanding built via CLIP neural network.
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Built an ML library that can describe an image or find relevant images for a sentence
Built [CLIP-as-service](https://github.com/jina-ai/clip-as-service), an open-source library to create embeddings of images and text using CLIP.
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[P] Clip-as-service to embed images and sentences into fixed-length vectors with CLIP
Excited to share my new project CLIP-as-service, a high-scalability service for embedding images and text. It serve CLIP models with ONNX runtime and PyTorch JIT with 800QPS.
What are some alternatives?
Typesense - Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
MoTIS - [NAACL 2022]Mobile Text-to-Image search powered by multimodal semantic representation models(e.g., OpenAI's CLIP)
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
laion-aesthetic-datasette - Use Datasette to explore LAION improved_aesthetics_6plus training data used by Stable DIffusion
DeBERTa - The implementation of DeBERTa
open_clip - An open source implementation of CLIP.
rclip - AI-Powered Command-Line Photo Search Tool
clip-italian - CLIP (Contrastive Language–Image Pre-training) for Italian
spaCy - đź’« Industrial-strength Natural Language Processing (NLP) in Python
Queryable - Run OpenAI's CLIP model on iOS to search photos.
electra - ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators