robin-qwen2.5-3b

Synthetic Visual Genome

This repository contains the ROBIN-3B model based on Qwen2.5-3B, introduced in the paper Synthetic Visual Genome. It is designed for scene graph understanding and dense visual relationships.

🤖 Checkpoints

• Robin-3b Stage 2 [this repo]: 🤗 hf-model
• Robin-3b Stage 1: TBD
• Robin-3b Stage 0: TBD

🚀 Quick Start: Scene Graph Generation with SAM

Generate scene graph for each image, using segment-anything masks and optional GroundingDINO object regions

1. First install Segment Anything

pip install git+https://github.com/facebookresearch/segment-anything.git

1. Download all the checkpoints:

• ViT-H SAM model
• Robin-3b
  • Run git clone https://huggingface.co/jamepark3922/robin-qwen2.5-3b
• CLIP-convnext

The default path of all the checkpoints:

├── demo
    ├── checkpoints
    │   ├── robin-qwen2.5-3b-sg-stage2
    │   └── sam_vit_h_4b8939.pth 
    └── open_clip_pytorch_model.bin

Note: You might need to change the "mm_vision_tower" in config.json of robin-3b model to the Absolute path of open_clip_pytorch_model.bin.

Scene Graph Generation for Single Image 🖼️

Refer to the SyntheticVG repo for the full code to generate scene graph for a single image using SAM and Robin-3b.

import requests

from segment_anything import sam_model_registry

from svg.pipeline.region_proposal.region_generator import SamGroundingDinoRegionGenerator
from svg.pipeline.grounding.grounding_dino import GroundingDinoSAM
from svg.pipeline.captioning.gpt4o import GPT4Captioner
from svg.pipeline.robin import RobinPipeline
from svg.draw_utils import  visualize_masks

image = Image.open(requests.get('http://farm4.staticflickr.com/3377/3573516590_a1f6cf2cbd_z.jpg', stream=True).raw)

device = 'cuda' if torch.cuda.is_available() else 'cpu'

sam_ckpt = 'sam_vit_h_4b8939.pth'
sam_model = sam_model_registry["vit_h"](checkpoint=sam_ckpt).to(device)

# Optional: grounding_dino + gpt4o captioner for additional region grounding
print('Loading GroundingDino model...')
grounding_model = GroundingDinoSAM(
    "IDEA-Research/grounding-dino-base", 
    sam_model, 
    device
)
captioner = GPT4Captioner()
region_generator = SamGroundingDinoRegionGenerator(
    sam_model=sam_model,
    grounding_model=grounding_model, # None if not using
    captioner=captioner
)
regions: list[dict] = region_generator.generate_regions(image, region_mode='merged')

# Generate scene graph from regions
model = RobinPipeline(robin_path, device=device)
sg, _ = model.generate_scene_graph(im, regions)
objects: list[str] = sg['objects']
relations: list[tuple[int, int, str]] = sg['relations']

# Visualize the scene graph
image_rgb = np.array(image)
image_with_masks: np.ndarray = visualize_masks(
    image_rgb, regions, 
    draw_bbox=True, draw_mask = True, draw_polygon=False,
    white_padding=50
    )
cv2.imwrite('scene_graph.jpg', image_with_masks)
with open('scene_graph.json', 'w') as f:
    json.dump(scene_graph, f, indent=4)

You can also run predict.py to generate scene graph for a single image.

python predict.py --image_path path/to/image.jpg

BibTeX 🖊️

If you find this work useful, please consider citing:

@misc{park2025syntheticvisualgenome,
      title={Synthetic Visual Genome}, 
      author={Jae Sung Park and Zixian Ma and Linjie Li and Chenhao Zheng and Cheng-Yu Hsieh and Ximing Lu and Khyathi Chandu and Quan Kong and Norimasa Kobori and Ali Farhadi and Yejin Choi and Ranjay Krishna},
      year={2025},
      eprint={2506.07643},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.07643}, 
}