Back2Struct-Image2SVG-7B API & Inference Endpoint

🤖 Model — `Back2Struct-Image2SVG-7B`

Table

Base	`Qwen/Qwen2.5-VL-7B-Instruct` (7B)
Stage 1 — SFT	Supervised fine-tuning on StructHub image→SVG pairs
Stage 2 — GRPO	Group-Relative Policy Optimization (TRL)
Reward	Composite, render-gated: syntactic validity (must render) × perceptual fidelity (baseline-subtracted DINOv2 similarity between the rendered prediction and the target), encouraging compilability, length consistency, and structural/semantic faithfulness
Max output	8,192 tokens · LoRA (r=64, α=128), merged into the released weights

📚 Dataset — `StructHub`

Structured images paired with their editable SVG/XML source code.

Table with columns: Split, Description, # examples
Split	Description	# examples
`train`	Full training set (all sources, all token lengths), benchmark held out	86,102
`benchmark`	Held-out evaluation benchmark (starvector 569 + crawled 334 + nn_diagram 97)	1,000

Fields: id (string), source (string), token_count (int32), image (embedded PNG), svg (ground-truth SVG/XML). The benchmark is stratified by SVG length into easy (≤2,048), medium (2,048–4,096), and hard (>4,096) tiers (333/333/334).

Results

StructHub benchmark, render-gated (a non-rendering prediction scores 0 on DINO/SSIM/GPT, 1 on LPIPS). SR = render success (%); GPT = 0–100 GPTScore.

Table with columns: Model, SR ↑, DINO ↑, LPIPS ↓, SSIM ↑, GPT ↑
Model	SR ↑	DINO ↑	LPIPS ↓	SSIM ↑	GPT ↑
Overall
Qwen3-VL-30B-A3B	18.8	0.1566	0.9067	0.1201	13.29
StarVector-8B	31.4	0.2694	0.7936	0.2170	21.05
Qwen2.5-VL-7B (base)	52.3	0.3527	0.7834	0.3276	27.56
Qwen2.5-VL-7B (SFT)	42.8	0.3715	0.7403	0.2891	33.49
Back2Struct (RL)	71.5	0.5622	0.6473	0.4966	51.85
Easy
Qwen3-VL-30B-A3B	26.5	0.2231	0.8659	0.1702	19.51
StarVector-8B	46.1	0.3923	0.7023	0.3123	31.90
Qwen2.5-VL-7B (base)	69.3	0.4776	0.7123	0.4196	39.66
Qwen2.5-VL-7B (SFT)	56.0	0.4866	0.6630	0.3789	44.58
Back2Struct (RL)	84.0	0.6384	0.5956	0.5796	62.63
Medium
Qwen3-VL-30B-A3B	19.6	0.1631	0.9004	0.1288	13.38
StarVector-8B	37.0	0.3232	0.7533	0.2583	25.27
Qwen2.5-VL-7B (base)	51.8	0.3546	0.7816	0.3306	26.88
Qwen2.5-VL-7B (SFT)	45.5	0.3985	0.7161	0.3091	36.50
Back2Struct (RL)	75.6	0.6088	0.6182	0.5275	55.14
Hard
Qwen3-VL-30B-A3B	10.2	0.0836	0.9536	0.0614	7.01
StarVector-8B	11.1	0.0932	0.9249	0.0808	6.02
Qwen2.5-VL-7B (base)	35.7	0.2263	0.8561	0.2331	16.17
Qwen2.5-VL-7B (SFT)	27.0	0.2297	0.8413	0.1795	19.43
Back2Struct (RL)	55.0	0.4399	0.7278	0.3830	37.83

On successfully compiled outputs, the 7B model matches or beats frontier closed models:

Table with columns: Model, DINO ↑, LPIPS ↓, SSIM ↑, GPT ↑
Model	DINO ↑	LPIPS ↓	SSIM ↑	GPT ↑
Gemini-2.5-Pro	0.8855	0.4648	0.6532	77.95
GPT-5	0.8852	0.4350	0.6573	77.21
Back2Struct (7B)	0.8697	0.3896	0.6738	78.63

Usage

Run the model

python

import torchfrom transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_info model_id = "Pengyu965/Back2Struct-Image2SVG-7B"model = Qwen2_5_VLForConditionalGeneration.from_pretrained(    model_id, torch_dtype=torch.bfloat16, device_map="auto")processor = AutoProcessor.from_pretrained(model_id) messages = [{"role": "user", "content": [    {"type": "image", "image": "diagram.png"},    {"type": "text",  "text": "Convert this structured image into editable SVG/XML code."},]}]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)image_inputs, video_inputs = process_vision_info(messages)inputs = processor(text=[text], images=image_inputs, videos=video_inputs,                   padding=True, return_tensors="pt").to(model.device)generated = model.generate(**inputs, max_new_tokens=8192, do_sample=False)print(processor.batch_decode(generated[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0])

Load the dataset

python

from datasets import load_datasetds = load_dataset("Pengyu965/StructHub")ex = ds["benchmark"][0]ex["image"].save("input.png"); print(ex["svg"][:500])

Intended use & limitations

Intended use: recovering editable vector code from images of structured graphics.
Not for: photographic / natural-image vectorization.
Limitations: built on a general (non-code-specialized) VLM; outputs capped at 8,192 tokens; very long/dense diagrams (hard tier) remain the main failure mode (render reliability).

Licensing

Model weights are released under Apache-2.0 (inheriting the Qwen2.5-VL-7B-Instruct base). The StructHub dataset aggregates multiple sources, including crawled open-domain graphics; it is released for research use — review and respect the original content licenses. (Confirm before wide distribution.)

Citation

bibtex

@misc{yan2026back2structmakingstructuredimages,      title={Back2Struct: Making Structured Images Editable Again},      author={Pengyu Yan and Yixin Wu and Yunjie Tian and David Doermann},      year={2026},      eprint={2609.37016},      archivePrefix={arXiv},      primaryClass={cs.CV},      url={https://arxiv.org/abs/2609.37016},}

Base

Qwen/Qwen2.5-VL-7B-Instruct (7B)

Stage 1 — SFT

Supervised fine-tuning on StructHub image→SVG pairs

Stage 2 — GRPO

Group-Relative Policy Optimization (TRL)

Reward

Composite, render-gated: syntactic validity (must render) × perceptual fidelity (baseline-subtracted DINOv2 similarity between the rendered prediction and the target), encouraging compilability, length consistency, and structural/semantic faithfulness

Max output

8,192 tokens · LoRA (r=64, α=128), merged into the released weights

Split

Description

# examples

train

Full training set (all sources, all token lengths), benchmark held out

86,102

benchmark

Held-out evaluation benchmark (starvector 569 + crawled 334 + nn_diagram 97)

1,000

Model

SR ↑

DINO ↑

LPIPS ↓

SSIM ↑

GPT ↑

Overall

Qwen3-VL-30B-A3B

18.8

0.1566

0.9067

0.1201

13.29

StarVector-8B

31.4

0.2694

0.7936

0.2170

21.05

Qwen2.5-VL-7B (base)

52.3

0.3527

0.7834

0.3276

27.56

Qwen2.5-VL-7B (SFT)

42.8

0.3715

0.7403

0.2891

33.49

Back2Struct (RL)

71.5

0.5622

0.6473

0.4966

51.85

Easy

Qwen3-VL-30B-A3B

26.5

0.2231

0.8659

0.1702

19.51

StarVector-8B

46.1

0.3923

0.7023

0.3123

31.90

Qwen2.5-VL-7B (base)

69.3

0.4776

0.7123

0.4196

39.66

Qwen2.5-VL-7B (SFT)

56.0

0.4866

0.6630

0.3789

44.58

Back2Struct (RL)

84.0

0.6384

0.5956

0.5796

62.63

Medium

Qwen3-VL-30B-A3B

19.6

0.1631

0.9004

0.1288

13.38

StarVector-8B

37.0

0.3232

0.7533

0.2583

25.27

Qwen2.5-VL-7B (base)

51.8

0.3546

0.7816

0.3306

26.88

Qwen2.5-VL-7B (SFT)

45.5

0.3985

0.7161

0.3091

36.50

Back2Struct (RL)

75.6

0.6088

0.6182

0.5275

55.14

Hard

Qwen3-VL-30B-A3B

10.2

0.0836

0.9536

0.0614

7.01

StarVector-8B

11.1

0.0932

0.9249

0.0808

6.02

Qwen2.5-VL-7B (base)

35.7

0.2263

0.8561

0.2331

16.17

Qwen2.5-VL-7B (SFT)

27.0

0.2297

0.8413

0.1795

19.43

Back2Struct (RL)

55.0

0.4399

0.7278

0.3830

37.83

Model

DINO ↑

LPIPS ↓

SSIM ↑

GPT ↑

Gemini-2.5-Pro

0.8855

0.4648

0.6532

77.95

GPT-5

0.8852

0.4350

0.6573

77.21

Back2Struct (7B)

0.8697

0.3896

0.6738

78.63

import torchfrom transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_info model_id = "Pengyu965/Back2Struct-Image2SVG-7B"model = Qwen2_5_VLForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto")processor = AutoProcessor.from_pretrained(model_id) messages = [{"role": "user", "content": [ {"type": "image", "image": "diagram.png"}, {"type": "text", "text": "Convert this structured image into editable SVG/XML code."},]}]text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)image_inputs, video_inputs = process_vision_info(messages)inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt").to(model.device)generated = model.generate(**inputs, max_new_tokens=8192, do_sample=False)print(processor.batch_decode(generated[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0])

@misc{yan2026back2structmakingstructuredimages, title={Back2Struct: Making Structured Images Editable Again}, author={Pengyu Yan and Yixin Wu and Yunjie Tian and David Doermann}, year={2026}, eprint={2609.37016}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2609.37016},}

Back2Struct-Image2SVG-7B

README

🤖 Model — `Back2Struct-Image2SVG-7B`

📚 Dataset — `StructHub`

Results

Usage

Run the model

Load the dataset

Intended use & limitations

Licensing

Citation

Explore FriendliAI today

README

🤖 Model — `Back2Struct-Image2SVG-7B`

📚 Dataset — `StructHub`

Results

Usage

Run the model

Load the dataset

Intended use & limitations

Licensing

Citation

Back2Struct-Image2SVG-7B

README

🤖 Model — Back2Struct-Image2SVG-7B

📚 Dataset — StructHub

Results

Usage

Run the model

Load the dataset

Intended use & limitations

Licensing

Citation

Explore FriendliAI today

README

🤖 Model — Back2Struct-Image2SVG-7B

📚 Dataset — StructHub

Results

Usage

Run the model

Load the dataset

Intended use & limitations

Licensing

Citation

🤖 Model — `Back2Struct-Image2SVG-7B`

📚 Dataset — `StructHub`

🤖 Model — `Back2Struct-Image2SVG-7B`

📚 Dataset — `StructHub`