examples
harlequin
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examples | harlequin | |
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5 | 13 | |
376 | 2,452 | |
10.9% | - | |
6.8 | 9.2 | |
3 months ago | 6 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | MIT License |
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examples
- FLaNK Stack Weekly for 07August2023
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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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Ask HN: Any good self-hosted image recognition software?
Usually this is done in three steps. The first step is using a neural network to create a bounding box around the object, then generating vector embeddings of the object, and then using similarity search on vector embeddings.
The first step is accomplished by training a detection model to generate the bounding box around your object, this can usually be done by finetuning an already trained detection model. For this step the data you would need is all the images of the object you have with a bounding box created around it, the version of the object doesnt matter here.
The second step involves using a generalized image classification model thats been pretrained on generalized data (VGG, etc.) and a vector search engine/vector database. You would start by using the image classification model to generate vector embeddings (https://frankzliu.com/blog/understanding-neural-network-embe...) of all the different versions of the object. The more ground truth images you have, the better, but it doesn't require the same amount as training a classifier model. Once you have your versions of the object as embeddings, you would store them in a vector database (for example Milvus: https://github.com/milvus-io/milvus).
Now whenever you want to detect the object in an image you can run the image through the detection model to find the object in the image, then run the sliced out image of the object through the vector embedding model. With this vector embedding you can then perform a search in the vector database, and the closest results will most likely be the version of the object.
Hopefully this helps with the general rundown of how it would look like. Here is an example using Milvus and Towhee https://github.com/towhee-io/examples/tree/3a2207d67b10a246f....
Disclaimer: I am a part of those two open source projects.
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Deep Dive into Real-World Image Search Engine with Python
I have shown how to Build an Image Search Engine in Minutes in the previous tutorial. Here is another one for how to optimize the algorithm, feed it with large-scale image datasets, and deploy it as a micro-service.
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Build an Image Search Engine in Minutes
The full tutorial is at https://github.com/towhee-io/examples/blob/main/image/reverse_image_search/build_image_search_engine.ipynb
harlequin
- DBeaver – open-source Database client
- FLaNK Stack 29 Jan 2024
- FLaNK Weekly 08 Jan 2024
- Harlequin: SQL IDE for Your Terminal
- Harlequin: DuckDB IDE for the terminal
- Harlequin.sh DuckDB IDE for your terminal
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Show HN: Harlequin, the DuckDB IDE for Your Terminal
For the past four months I've been working (part-time, this is OSS after all) on Harlequin, a SQL IDE for DuckDB that runs in your terminal. I built this because I work in Data, and I found myself often reaching for the DuckDB CLI to quickly query CSV or Parquet data, but then hitting a wall when using the DuckDB CLI as my queries got more complex and my result sets got larger.
Harlequin is a drop-in replacement for the DuckDB CLI that runs in any terminal (even over SSH), but adds a browsable data catalog, full-powered text editor (with multiple buffer support), and a scrollable results viewer that can display thousands of records.
Harlequin is written in Python, using the Textual framework. It's licensed under MIT.
Today I released v1.0.0, and I'm excited to share Harlequin with HN for the first time. You can try it out with `pip install harlequin`, or visit https://harlequin.sh for docs and other info.
- FLaNK Stack Weekly for 07August2023
What are some alternatives?
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
hugging-chat-api - HuggingChat Python API🤗
milvus-lite - A lightweight version of Milvus wrapped with Python.
opensms - Open-source solution to programmatically send and receive SMS using your own SIM cards
gorilla-cli - LLMs for your CLI
llama2_aided_tesseract - Enhance Tesseract OCR output for scanned PDFs by applying Large Language Model (LLM) corrections, complete with options for text validation and hallucination filtering.
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
OpenBuddy - Open Multilingual Chatbot for Everyone
EverythingApacheNiFi - EverythingApacheNiFi
textadept - Textadept is a fast, minimalist, and remarkably extensible cross-platform text editor for programmers.
Qwen-7B - The official repo of Qwen (通义千问) chat & pretrained large language model proposed by Alibaba Cloud. [Moved to: https://github.com/QwenLM/Qwen]