imgbeddings
memq
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imgbeddings | memq | |
---|---|---|
8 | 2 | |
122 | 111 | |
- | 3.6% | |
0.0 | 6.4 | |
about 2 years ago | 5 days ago | |
Python | Java | |
MIT License | 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.
imgbeddings
- FLaNK Stack Weekly for 20 Nov 2023
-
Content-Based Image Retrieval
Seconding the recommendation of CLIP embeddings, especially compared to image histograms + requiring OpenCV.
I wrote a naive, minimal dependency Python package to calculate image embeddings (https://github.com/minimaxir/imgbeddings) with some lookup demo notebooks and it works well in a pinch, although it's due for an upgrade.
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How to build a working AI only using synthetic data in just 5 minutes
Normally, this is Hacker News reductiveness, but yes, image classification via CLIP is that easy, especially with Hugging Face's API for it: https://huggingface.co/docs/transformers/model_doc/clip
I created a Python package to generate image embeddings from CLIP's vision model (without requiring a ML framework), and a simple linear classifier on those embeddings does the trick: https://github.com/minimaxir/imgbeddings
- GitHub - minimaxir/imgbeddings: Python package to generate image embeddings with CLIP without PyTorch/TensorFlow
- Show HN: Python package to create image embeddings without PyTorch/TensorFlow
- I've released a Python package which lets you generate vector representations of images clustering/similarity search/classifier building with a twist: neither PyTorch nor TensorFlow is used!
- [P] I've released a Python package which lets you generate vector representations of images with a twist: neither PyTorch nor TensorFlow is used!
- Show HN: Python package to create image embeddings with o PyTorch/TensorFlow
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