I spent the past 3 weeks building x.infer, a Python package that lets you run computer vision inference on a framework of choice.
It currently supports models from transformers, Ultralytics, Timm, vLLM and Ollama. Combined, this covers over 1000+ computer vision models. You can easily add your own model.
It's mostly just for fun. I wanted to practice some design pattern principles I picked up from the past. The code is still messy though but it works.
Also, I enjoy playing around with new vision models, but not so much learning about the framework it's written with.
I'm working on this during my free time. Contributions/feedback are more than welcome! Hope this also helps you (especially newcomers) to experiment and play around with new vision models.
It currently supports models from transformers, Ultralytics, Timm, vLLM and Ollama. Combined, this covers over 1000+ computer vision models. You can easily add your own model.
Repo - https://github.com/dnth/x.infer
Colab quickstart - https://colab.research.google.com/github/dnth/x.infer/blob/m...
Why did I make this?
It's mostly just for fun. I wanted to practice some design pattern principles I picked up from the past. The code is still messy though but it works.
Also, I enjoy playing around with new vision models, but not so much learning about the framework it's written with.
I'm working on this during my free time. Contributions/feedback are more than welcome! Hope this also helps you (especially newcomers) to experiment and play around with new vision models.