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NikshepShetty/
Florence-2-Recap-DataComp

Name: Florence-2-Recap-DataComp
Availability: InStock

UCSC-VLAA/Recap-DataComp-1B

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Florence-2 Recap-DataComp LoRA Adapter

This repository contains a LoRA adapter trained on the UCSC-VLAA/Recap-DataComp-1B dataset for the Florence-2-base-FT model. It's designed to enhance the model's captioning capabilities, providing more detailed and descriptive image 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:

python

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-Recap-DataComp"
    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:

Load the base Florence-2 model
Load the LoRA adapter
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	0.553	+1.3%
METEOR	0.213	0.240	+12.7%
BLEU	0.110	0.150	+36.4%
CIDEr	0.031	0.035	+12.9%
ROUGE-L	0.275	0.294	+6.9%

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.