NikshepShetty/Florence-2-pixelprose

Florence-2 PixelProse LoRA Adapter

This repository contains a LoRA adapter trained on the tomg-group-umd/pixelprose 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:

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-pixelprose"
    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	0.555	+1.6%
METEOR	0.213	0.250	+17.4%
BLEU	0.110	0.155	+40.9%
CIDEr	0.031	0.039	+25.8%
ROUGE-L	0.275	0.298	+8.4%

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.