Merve Noyan PRO

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SegGPT is a vision generalist on image segmentation, quite like GPTs for computer vision ✨
It comes with the last release of transformers 🎁 Demo and more in this post!
SegGPT is an extension of the Painter, where you speak to images with images: the model takes in an image prompt, transformed version of the image prompt, the actual image you want to see the same transform, and expected to output the transformed image.
SegGPT consists of a vanilla ViT with a decoder on top (linear, conv, linear).
The model is trained on diverse segmentation examples, where they provide example image-mask pairs, the actual input to be segmented, and the decoder head learns to reconstruct the mask output.
This generalizes pretty well!
The authors do not claim state-of-the-art results as the model is mainly used zero-shot and few-shot inference. They also do prompt tuning, where they freeze the parameters of the model and only optimize the image tensor (the input context).
Thanks to πŸ€— transformers you can use this model easily!
See here https://huggingface.co/docs/transformers/en/model_doc/seggpt
I have built an app for you to try it out. I combined SegGPT with Depth Anything Model, so you don't have to upload image mask prompts in your prompt pair πŸ€—
Try it here merve/seggpt-depth-anything
Also check out the collection merve/seggpt-660466a303bc3cd7559d271b
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LLaVA-NeXT is recently merged to Hugging Face transformers and it outperforms many of the closed source models like Gemini on various benchmarks 🀩 Let's take a look!
Demo: merve/llava-next
Notebook: https://colab.research.google.com/drive/1afNudu72SNWZCYtCVrRlb9T9Vj9CFJEK?usp=sharing
LLaVA is essentially a vision-language model that consists of ViT-based CLIP encoder, a MLP projection and Vicuna as decoder ✨
LLaVA 1.5 was released with Vicuna, but LLaVA NeXT (1.6) is released with four different LLMs:
- Nous-Hermes-Yi-34B
- Mistral-7B
- Vicuna 7B & 13B
Mistral and Nous-Hermes-Yi-34B are performing better and have better commercial use.
Moreover, according to authors' findings, the improvements comes from more diverse and high quality data mixture and dynamic high resolution.
LLaVA based on Nous-Hermes-Yi-34B outperforms many other models, including Gemini in various multimodal understanding and generation benchmarks 😊