content.raw: Florence-2 is a new vision foundation model capable of a wide variety of tasks 🤯 
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content.value: Florence-2 is a new vision foundation model capable of a wide variety of tasks 🤯 
content.type: new_line
content.raw: Demo 👉🏻 
content.type: text
content.value: Demo 👉🏻 
content.raw: https://huggingface.co/spaces/gokaygokay/Florence-2
content.id: gokaygokay/Florence-2
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content.url: https://huggingface.co/spaces/gokaygokay/Florence-2
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content.raw: Collection 👉🏻 
content.type: text
content.value: Collection 👉🏻 
content.raw: https://huggingface.co/collections/microsoft/florence-6669f44df0d87d9c3bfb76de
content.id: microsoft/florence-6669f44df0d87d9c3bfb76de
content.type: collection
content.type: resource
content.url: https://huggingface.co/collections/microsoft/florence-6669f44df0d87d9c3bfb76de
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content.raw: This model can handle tasks that vary from OCR to semantic segmentation. 
content.type: text
content.value: This model can handle tasks that vary from OCR to semantic segmentation. 
content.type: new_line
content.type: new_line
content.raw: The difference from previous models is that the authors have compiled a dataset consisting of 126M images with 5.4B annotations labelled with their own data engine pseudolabelled by smaller specialized models and APIs. 
content.type: text
content.value: The difference from previous models is that the authors have compiled a dataset consisting of 126M images with 5.4B annotations labelled with their own data engine pseudolabelled by smaller specialized models and APIs. 
content.type: new_line
content.type: new_line
content.raw: The model has a similar architecture to previous models: an image encoder and a multimodality encoder with a text decoder. The authors have compiled the multitask dataset with prompts for each task. 
content.type: text
content.value: The model has a similar architecture to previous models: an image encoder and a multimodality encoder with a text decoder. The authors have compiled the multitask dataset with prompts for each task. 
content.type: new_line
content.type: new_line
content.raw: You can also fine-tune this model on any task of choice. The authors also released different results on downstream tasks and reported their results when un/freezing the vision encoder 🤓📉 
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content.value: You can also fine-tune this model on any task of choice. The authors also released different results on downstream tasks and reported their results when un/freezing the vision encoder 🤓📉 
content.type: new_line
content.raw: They have released fine-tuned models too, you can find them in the collection above 🤗 
content.type: text
content.value: They have released fine-tuned models too, you can find them in the collection above 🤗 
rawContent: Florence-2 is a new vision foundation model capable of a wide variety of tasks 🤯 
  Demo 👉🏻 https://huggingface.co/spaces/gokaygokay/Florence-2
  Collection 👉🏻 https://huggingface.co/collections/microsoft/florence-6669f44df0d87d9c3bfb76de
  
  This model can handle tasks that vary from OCR to semantic segmentation. 
  
  The difference from previous models is that the authors have compiled a dataset consisting of 126M images with 5.4B annotations labelled with their own data engine pseudolabelled by smaller specialized models and APIs. 
  
  The model has a similar architecture to previous models: an image encoder and a multimodality encoder with a text decoder. The authors have compiled the multitask dataset with prompts for each task. 
  
  You can also fine-tune this model on any task of choice. The authors also released different results on downstream tasks and reported their results when un/freezing the vision encoder 🤓📉 
  They have released fine-tuned models too, you can find them in the collection above 🤗 
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