Florence 2 Large

--- license: mit license_link: https://huggingface.co/microsoft/Florence-2-large/resolve/main/LICENSE pipeline_tag: image-text-to-text tags: - vision --- # Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks ## Model Summary **This is a continued pretrained version of Florence-2-large model with 4k context length, only 0.1B samples are used for continue pretraining, thus it might not be trained well. In addition, OCR task has been updated with line separator ('\n'). COCO OD AP 39.8** This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. Resources and Technical Documentation: + [Florence-2 technical report](https://arxiv.org/abs/2311.06242). + [Jupyter Notebook for inference and visualization of Florence-2-large](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb) | Model | Model size | Model Description | | ------- | ------------- | ------------- | | Florence-2-base[[HF]](https://huggingface.co/microsoft/Florence-2-base) | 0.23B | Pretrained model with FLD-5B | Florence-2-large[[HF]](https://huggingface.co/microsoft/Florence-2-large) | 0.77B | Pretrained model with FLD-5B | Florence-2-base-ft[[HF]](https://huggingface.co/microsoft/Florence-2-base-ft) | 0.23B | Finetuned model on a colletion of downstream tasks | Florence-2-large-ft[[HF]](https://huggingface.co/microsoft/Florence-2-large-ft) | 0.77B | Finetuned model on a colletion of downstream tasks ## How to Get Started with the Model Use the code below to get started with the model. All models are trained with float16. ```python import requests import torch from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_av...