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Google's PaliGemma, a vision-language model, is gaining traction in the AI community for its versatility in fine-tuning for various tasks. PaliGemma can be trained using datasets like VQAv2 and is particularly useful as a backbone model for vision-language tasks rather than zero-shot applications. Tutorials and resources are available for fine-tuning PaliGemma on custom datasets using platforms like Google Colab, with tutorials covering aspects such as model quantization and LoRa. Fine-tuned models can be saved to Hugging Face. Additionally, PaliGemma is powering open-source projects like YoloGemma, aimed at enhancing computer vision tasks such as object detection. An example use case includes converting images to JSON.
Awesome tutorial by @NielsRogge on fine-tuning PaliGemma, a google vision-language model, on image to JSON use cases! Love the simplicity! https://t.co/DjypFB6AXE
PaliGemma in a nutshell: not made for zero-shot but rather use it as a backbone to fine-tune for any vision-language task https://t.co/aV92Rai866
Alright finally back on @YouTube with a new video: fine-tuning PaliGemma (or LLaVa, Idefics2,...) on your custom dataset! I'm fine-tuning in @GoogleColab on an L4 GPU I go over many things like how the model actually works, LoRa, quantization and more! https://t.co/YKiu2FAAA0 https://t.co/SpB2vdudQu
Open sourcing YoloGemma an attempt at using vision language models for computer vision tasks like object detection powered by Paligemma https://t.co/eBFoVhP7s5
How to Fine Tune Google PaliGemma, a Vision Language Model? 📸 Train PaliGemma with VQAv2 dataset 📚 Load datasets 🔧 Model training steps 🚀 Save to Hugging Face Subscribe: https://t.co/RTY3pSWdvT YT: https://t.co/dqQzf0eke7 @Google @GoogleAI @googleeurope @googledevs https://t.co/VFimmTliwI