COinCO/Qwen2.5-VL-3B-Size

Qwen2.5-VL-3B Size Context Classifier

Authors: Tianze Yang\, Tyson Jordan\, Ruitong Sun\, Ninghao Liu, Jin Sun \Equal contribution | Affiliation: University of Georgia

Overview

A fine-tuned Qwen2.5-VL-3B-Instruct model for detecting out-of-context objects based on the size criterion.

Given an image with an object marked by a red bounding box, the model evaluates whether the object's size is proportional and realistic relative to other objects and the environment. Objects that are abnormally large or small compared to their surroundings are classified as out-of-context.

How to Use

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
import torch

model_id = "COinCO/Qwen2.5-VL-3B-Size"

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)

Training Details

Evaluation Results

Inpainted Test Set (In-context vs Out-of-context)

Real COCO Images (shortcut learning detection, higher = better)

The baseline model performs near random chance (50.21%) on size classification and heavily relies on visual artifacts. After fine-tuning, performance improves significantly on both inpainted and real images.

Related Resources

• —Dataset: COinCO/COinCO-dataset
• —Code: YangTianze009/COinCO
• —Other models: Co-occurrence | Location

Citation

@article{yang2025coinco,
  title={Common Inpainted Objects In-N-Out of Context},
  author={Tianze Yang and Tyson Jordan and Ruitong Sun and Ninghao Liu and Jin Sun},
  year={2025}
}