towhee
benchmark
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towhee | benchmark | |
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26 | 1 | |
2,951 | 7 | |
1.1% | - | |
8.6 | 0.0 | |
3 months ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | - |
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.
towhee
- FLaNK Stack Weekly for 14 Aug 2023
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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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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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[P] Working on unstructured data?
image_decode and img2img_translation.animegan are predefined operators from towhee hub. We already have nearly a hundred operators, officially maintained or contributed by our users.
We have just released Towhee 0.6, a framework for doing ML jobs over unstructured data. Our latest release includes DataCollection, a new user-centric method-chaining API that enables rapid development and prototyping of embedding applications.
benchmark
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[N] We just got funded for an open-source project to make Metric Learning practical.
Regarding Milvus. Well, there are a few essential differences between our projects: - Unlike Milvus, we perform filtering during the search in the vector index, which keeps retrieval complexity close to logarithmic - same as in original HNSW. - We can support complex types of filterable payloads like geo-points - it is not a trivial problem to keep the HNSW search graph connected during filtering. We solved it in our custom implementation of the HNSW index. - Unlike Milvus, we perform a query-planning phase to determine an optimal strategy of executing queries with filters - Qdrant uses Rust programming language - it gives us an advantage in avoiding stop-the-world issues of languages with garbage collection. We also have a retrieval speed benchmark - https://github.com/qdrant/benchmark.
What are some alternatives?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Milvus - A cloud-native vector database, storage for next generation AI applications
examples - Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
PySceneDetect - :movie_camera: Python and OpenCV-based scene cut/transition detection program & library.
AI - Artificial Intelligence Projects
awesome-embedding-models - A curated list of awesome embedding models tutorials, projects and communities.
pgvector - Open-source vector similarity search for Postgres
torchscale - Foundation Architecture for (M)LLMs
hyperparameter - Hyperparameter, Make configurable AI applications.Build for Python hackers.
pipelines - Create Async Processing Pipelines Quick!
marqo - Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
Dependency Injector - Dependency injection framework for Python