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Florence-2

docs/source/en/model_doc/florence2.md

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This model was released on 2024-06-16 and added to Hugging Face Transformers on 2025-08-20.

Florence-2

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Overview

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 the 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.

You can find all the original Florence-2 checkpoints under the Florence-2 collection.

[!TIP] This model was contributed by ducviet00. Click on the Florence-2 models in the right sidebar for more examples of how to apply Florence-2 to different vision and language tasks.

The example below demonstrates how to perform object detection with [Pipeline] or the [AutoModel] class.

<hfoptions id="usage"> <hfoption id="Pipeline">
python
from transformers import pipeline


pipeline = pipeline(
    "image-text-to-text",
    model="florence-community/Florence-2-base",
    device=0,
)

pipeline(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true",
    text="<OD>"
)
</hfoption> <hfoption id="AutoModel">
python
import requests
from PIL import Image

from transformers import AutoProcessor, Florence2ForConditionalGeneration


url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

model = Florence2ForConditionalGeneration.from_pretrained("florence-community/Florence-2-base", device_map="auto")
processor = AutoProcessor.from_pretrained("florence-community/Florence-2-base")

task_prompt = "<OD>"
inputs = processor(text=task_prompt, images=image, return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **inputs,
    max_new_tokens=1024,
    num_beams=3,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]

image_size = image.size
parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=image_size)
print(parsed_answer)
</hfoption> </hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to quantize the model to 4-bit.

python
# pip install bitsandbytes
import requests
import torch
from PIL import Image

from transformers import AutoProcessor, BitsAndBytesConfig, Florence2ForConditionalGeneration


quantization_config = BitsAndBytesConfig(load_in_4bit=True)

model = Florence2ForConditionalGeneration.from_pretrained(
    "florence-community/Florence-2-base",
    device_map="auto",
    quantization_config=quantization_config
)
processor = AutoProcessor.from_pretrained("florence-community/Florence-2-base")

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

task_prompt = "<OD>"
inputs = processor(text=task_prompt, images=image, return_tensors="pt").to(model.device, torch.bfloat16)

generated_ids = model.generate(
    **inputs,
    max_new_tokens=1024,
    num_beams=3,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]

image_size = image.size
parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=image_size)

print(parsed_answer)
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Notes

  • Florence-2 is a prompt-based model. You need to provide a task prompt to tell the model what to do. Supported tasks are:
    • <OCR>
    • <OCR_WITH_REGION>
    • <CAPTION>
    • <DETAILED_CAPTION>
    • <MORE_DETAILED_CAPTION>
    • <OD>
    • <DENSE_REGION_CAPTION>
    • <CAPTION_TO_PHRASE_GROUNDING>
    • <REFERRING_EXPRESSION_SEGMENTATION>
    • <REGION_TO_SEGMENTATION>
    • <OPEN_VOCABULARY_DETECTION>
    • <REGION_TO_CATEGORY>
    • <REGION_TO_DESCRIPTION>
    • <REGION_TO_OCR>
    • <REGION_PROPOSAL>
  • The raw output of the model is a string that needs to be parsed. The [Florence2Processor] has a [~Florence2Processor.post_process_generation] method that can parse the string into a more usable format, like bounding boxes and labels for object detection.

Resources

Florence2VisionConfig

[[autodoc]] Florence2VisionConfig

Florence2Config

[[autodoc]] Florence2Config

Florence2Processor

[[autodoc]] Florence2Processor - call

Florence2Model

[[autodoc]] Florence2Model - forward - get_image_features

Florence2ForConditionalGeneration

[[autodoc]] Florence2ForConditionalGeneration - forward - get_image_features

Florence2VisionBackbone

[[autodoc]] Florence2VisionBackbone - forward