Florence-2-large

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

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.
• Jupyter Notebook for inference and visualization of Florence-2-large

How to Get Started with the Model

Use the code below to get started with the model.

import torch
import requests
from PIL import Image
from transformers import AutoProcessor, Florence2ForConditionalGeneration, BitsAndBytesConfig

model = Florence2ForConditionalGeneration.from_pretrained(
    "florence-community/Florence-2-large",
    dtype=torch.bfloat16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("florence-community/Florence-2-large")

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 = ""
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)

Tasks

This model is capable of performing different tasks through changing the prompts.

First, let's define a function to run a prompt.

Here are the tasks Florence-2 could perform:

Caption

prompt = ""
run_example(prompt)

Detailed Caption

prompt = ""
run_example(prompt)

More Detailed Caption

prompt = ""
run_example(prompt)

Caption to Phrase Grounding

caption to phrase grounding task requires additional text input, i.e. caption.

Caption to phrase grounding results format: {'': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}}

task_prompt = ""
results = run_example(task_prompt, text_input="A green car parked in front of a yellow building.")

Object Detection

OD results format: {'': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['label1', 'label2', ...]} }

prompt = ""
run_example(prompt)

Dense Region Caption

Dense region caption results format: {'' : {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['label1', 'label2', ...]} }

prompt = ""
run_example(prompt)

Region proposal

Dense region caption results format: {'': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}}

prompt = ""
run_example(prompt)

OCR

prompt = ""
run_example(prompt)

OCR with Region

OCR with region output format: {'': {'quad_boxes': [[x1, y1, x2, y2, x3, y3, x4, y4], ...], 'labels': ['text1', ...]}}

prompt = ""
run_example(prompt)

Benchmarks

Florence-2 Zero-shot performance

The following table presents the zero-shot performance of generalist vision foundation models on image captioning and object detection evaluation tasks. These models have not been exposed to the training data of the evaluation tasks during their training phase.

The following table continues the comparison with performance on other vision-language evaluation tasks.