ai-exploits
imgbeddings
| ai-exploits | imgbeddings | |
|---|---|---|
| 6 | 8 | |
| 1,317 | 122 | |
| 6.9% | - | |
| 7.0 | 0.0 | |
| 15 days ago | about 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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ai-exploits
- RCE in Nvidia Triton Inference Server
- FLaNK Stack Weekly for 20 Nov 2023
-
AI-Exploits: Repo of multiple unauthenticated RCEs in AI tools
(I work for ProtectAI) We added a quick demo to the Readme [1]
[1] https://github.com/protectai/ai-exploits?tab=readme-ov-file#...
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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