Florence-2 DOCCI-FT LoRA Adapter

This repository contains a LoRA adapter trained on google/docci for the Florence-2-base-FT model. It's designed to enhance the model's captioning capabilities, providing more detailed captions.

Usage

To use this LoRA adapter, you'll need to load it along with the Florence-2-base model using the PEFT library. Here's an example of how to use it:

from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
from peft import PeftModel, PeftConfig
import requests

def caption(image):
  base_model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
  processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
  prompt = "<MORE_DETAILED_CAPTION>"
  adapter_name = "NikshepShetty/Florence-2-DOCCI-FT"
  model = PeftModel.from_pretrained(base_model, adapter_name, trust_remote_code=True)
  inputs = processor(text=prompt, images=image, return_tensors="pt")

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

  parsed_answer = processor.post_process_generation(generated_text, task="<MORE_DETAILED_CAPTION>", image_size=(image.width, image.height))

  print(parsed_answer)

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)
caption(image)

This code demonstrates how to:

  1. Load the base Florence-2 model
  2. Load the LoRA adapter
  3. Process an image and generate a detailed caption

Note: Make sure you have the required libraries installed: transformers, peft, einops, flash_attn, timm, Pillow, and requests.

Evaluation results

Our LoRA adapter shows improvements over the base Florence-2 model across all metrics for MORE_DETAILED_CAPTION tag for 1000 images on the foundation-multimodal-models/DetailCaps-4870 dataset:

Metric Base Model Adapted Model Improvement
CAPTURE 0.546 0.576 +5.5%
METEOR 0.213 0.267 +25.4%
BLEU 0.110 0.185 +68.2%
CIDEr 0.031 0.086 +177.4%
ROUGE-L 0.275 0.287 +4.4%

These results demonstrate that our LoRA adapter enhances the image captioning capabilities of the Florence-2 base model, particularly in generating more detailed and accurate captions.

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Evaluation results

  • meteor on foundation-multimodal-models/DetailCaps-4870
    self-reported
    0.267
  • bleu on foundation-multimodal-models/DetailCaps-4870
    self-reported
    0.185
  • cider on foundation-multimodal-models/DetailCaps-4870
    self-reported
    0.086
  • capture on foundation-multimodal-models/DetailCaps-4870
    self-reported
    0.576
  • rouge-l on foundation-multimodal-models/DetailCaps-4870
    self-reported
    0.287