image_feature_extraction
towhee
| image_feature_extraction | towhee | |
|---|---|---|
| 1 | 26 | |
| 0 | 3,010 | |
| - | 2.3% | |
| 0.0 | 8.6 | |
| almost 3 years ago | 4 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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image_feature_extraction
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Class or Functions?
It’s a little of both. You can check out https://github.com/giakou4/image_feature_extraction Each way I approach the issue, both ways seem correct to me. Classes seem a good way to organise but functions are easy for debugging.
towhee
- FLaNK Stack Weekly for 14 Aug 2023
- Welcome to generate your embeddings with Towhee
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Vector database built for scalable similarity search
As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.
A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).
towhee has an example to do exactly what you want with CLIP[1].
[0] - https://towhee.io/
[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...
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What Is DocArray?
The description of this is kind of confusing but I think the easiest way to understand it is that it is a data processing pipeline of sorts. Take unstructured data and apply transformation and computation. A similar project to this is Towhee (https://github.com/towhee-io/towhee). This project tries to simplify unstructured data processing and provides pretrained models and pipelines from their hub.
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[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Milvus also has incredible flexibility when it comes to choosing an indexing strategy, and we also have a library specifically meant to help vectorize a variety of data called Towhee (https://github.com/towhee-io/towhee).
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Deep Dive into Real-World Image Search Engine with Python
Benchmarking the models with towhee is as simple as:
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A quick tip on DataFrame.apply
The project's homepage is https://github.com/towhee-io/towhee, and you can find more about towhee by going through the documents.
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Build an Image Search Engine in Minutes
I made a tutorial for building an image search engine with python. The code example is as simple as 10 lines of code, using Towhee and Milvus To put images into the search engine:
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Any good libraries for feature extraction?
Traditionally, I've done this through PyTorch by adding a hook, but this requires knowledge of the model itself (i.e. model arch and layer names). I found https://github.com/Hironsan/awesome-embedding-models but it didn't provide many CV-focused open-source projects. There's also https://github.com/towhee-io/towhee which is great but more targeted towards application development.
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A python framework for unstructured data processing
You can check the result from the tutorial.
What are some alternatives?
fishington.io-bot - Fishington.io bot with OpenCV and NumPy
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
eulerian-remote-heartrate-detection - Remote heart rate detection through Eulerian magnification of face videos
Milvus - A cloud-native vector database, storage for next generation AI applications
GPT4RoI - GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
examples - Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
pykaldi - A Python wrapper for Kaldi
PySceneDetect - :movie_camera: Python and OpenCV-based scene cut/transition detection program & library.
Fast-Poisson-Image-Editing - A fast poisson image editing implementation that can utilize multi-core CPU or GPU to handle a high-resolution image input.
AI - Artificial Intelligence Projects
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
pgvector - Open-source vector similarity search for Postgres