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library_name: peft
base_model: microsoft/Florence-2-base-ft
tags:
- florence-2
- lora
- object-detection
- subplot-detection
license: mit
---
# plot-visualization-florence-2-lora-32
This is a LoRA fine-tuned version of [microsoft/Florence-2-base-ft](https://huggingface.co/microsoft/Florence-2-base-ft) for detecting subplots in scientific figures.
## Model Details
- **Base Model:** microsoft/Florence-2-base-ft
- **LoRA Rank:** 32
- **Task:** Object Detection (subplot detection)
- **Labels:** `quantitative plot`, `qualitative plot`
## Usage
```python
from pathlib import Path
from PIL import Image
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoProcessor
import torch
# Load model
base_model = "microsoft/Florence-2-base-ft"
processor = AutoProcessor.from_pretrained(base_model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
base_model,
trust_remote_code=True,
torch_dtype=torch.float16,
)
model = PeftModel.from_pretrained(model, "amayuelas/plot-visualization-florence-2-lora-32")
model = model.merge_and_unload()
model = model.to("cuda")
# Run inference
image = Image.open("your_image.jpg").convert("RGB")
prompt = "<OD>"
inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
inputs["pixel_values"] = inputs["pixel_values"].to(torch.float16)
with torch.no_grad():
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
num_beams=3,
use_cache=False,
)
output = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
print(output)
```
## Training
This model was fine-tuned using PEFT/LoRA on a dataset of scientific figures with annotated subplots.
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