ZwZ-7B is a fine-grained multimodal perception model built upon Qwen2.5-VL-7B. It is trained using Region-to-Image Distillation (R2I) combined with reinforcement learning, enabling superior fine-grained visual understanding in a single forward pass — no inference-time zooming or tool calling required. ZwZ-7B achieves leading performance on fine-grained perception benchmarks among open-source Qwen2.5-VL-7B-based models.

Models	General Perception	Specific Perception	OOD Generalization	Avg
ZoomBench	HR-4K	HR-8K	VStar	CV-B.	MME-RW-en	MME-RW-cn	GP-Avg	CountQA	ColorB.	MMStar	BabyVision
Closed-Source Models
GPT-5.1	47.22	67.00	65.25	70.16	84.22	64.04	55.57	64.78	31.41	83.43	71.60	13.92	59.44
Gemini-3-Flash	59.29	87.88	85.00	86.39	89.57	74.86	72.62	79.37	66.88	85.47	83.60	34.51	75.10
Open-Source Models
Qwen3-VL-2B	41.30	71.75	70.12	72.77	78.94	59.52	60.77	65.02	22.19	76.86	60.4	12.11	56.98
Qwen3-VL-4B	40.24	78.25	72.88	80.10	84.95	63.47	63.63	69.07	28.14	81.63	69.73	13.66	61.52
Qwen2.5-VL-7B	42.49	71.62	67.88	78.53	75.34	60.80	58.30	64.99	18.91	76.36	61.93	12.89	56.82
Qwen3-VL-8B	37.87	78.88	74.63	86.39	85.44	65.96	66.67	70.83	28.99	82.77	70.93	12.89	62.86
MiMo-VL-7B-RL	45.09	74.38	72.88	81.15	84.31	63.40	59.78	68.71	28.27	82.80	73.53	16.24	61.98
MiniCPM-V-4.5 (9B)	42.60	69.88	63.62	70.16	80.25	58.16	56.23	62.99	23.43	79.75	67.87	14.95	56.99
GLM-4.5V (108B)	49.23	81.63	74.88	83.25	87.59	66.04	60.71	71.90	35.93	84.59	75.87	15.72	65.04
Qwen3-VL-235B-A22B	49.11	84.50	81.62	87.96	86.72	67.07	65.29	74.61	40.58	85.62	76.33	18.30	67.55
Kimi-K2.5 (1T)	56.33	81.87	75.38	85.86	89.18	71.51	68.40	75.50	52.81	86.61	81.80	33.25	71.18
Our Models
ZwZ-2B (Ours)	53.49	77.00	75.38	82.72	83.36	65.61	65.39	71.85	21.60	79.37	63.40	17.78	62.28
ZwZ-4B (Ours)	55.74	81.75	79.50	92.67	87.90	68.52	68.09	76.31	30.82	83.08	71.13	16.24	66.86
ZwZ-7B (Ours)	55.62	75.38	73.25	88.48	79.83	66.21	66.96	72.25	20.72	80.82	63.40	15.98	62.42
ZwZ-8B (Ours)	58.11	84.38	82.00	91.10	87.40	69.87	70.59	77.64	32.40	83.59	73.13	16.75	68.12

How It Works

Traditional "Thinking-with-Images" methods zoom into regions of interest during inference, incurring high latency from repeated tool calls and visual re-encoding. ZwZ transforms zooming from an inference-time tool into a training-time primitive:

Zoom in to micro-cropped regions and let strong teacher models (Qwen3-VL-235B, GLM-4.5V) generate high-quality VQA data
Distill this region-grounded supervision back to the full image with explicit bounding-box overlays
Reinforce via RL training to enable single-glance fine-grained perception without tool use

Quickstart

Installation

pip install transformers accelerate torch qwen-vl-utils

Inference

python

from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "inclusionAI/ZwZ-7B", torch_dtype="auto", device_map="auto"
)

# default processer
processor = AutoProcessor.from_pretrained("inclusionAI/ZwZ-7B")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
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",
)
inputs = inputs.to("cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Training Data

ZwZ-7B is trained on inclusionAI/ZwZ-RL-VQA, a 74K-sample Region-to-Image distilled VQA dataset synthesized from diverse image pools (SA-1B, LAION, MetaCLIP, Visual Genome, CC12M, STPLS3D).

Evaluation

ZoomBench

We introduce ZoomBench, a challenging benchmark with 845 samples across 6 fine-grained dimensions: counting, OCR, color attributes, structural attributes, material attributes, and object identification.

Benchmark Results

ZwZ-7B achieves state-of-the-art performance among open-source models on fine-grained perception benchmarks. Please refer to the paper for detailed results.

Limitations

Performance is optimized for fine-grained perception tasks; for general-purpose multimodal understanding, the base model (Qwen2.5-VL-7B) may be more suitable in some cases.
Very high-resolution images may require appropriate preprocessing for optimal results.
The model inherits the base model's context length limitations.

Citation

bibtex

@article{wei2026zooming,
  title={Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception},
  author={Wei, Lai and He, Liangbo and Lan, Jun and Dong, Lingzhong and Cai, Yutong and Li, Siyuan and Zhu, Huijia and Wang, Weiqiang and Kong, Linghe and Wang, Yue and Zhang, Zhuosheng and Huang, Weiran},
  journal={arXiv preprint arXiv:2602.11858},
  year={2026}
}

License

This model follows the license of Apache License 2.0.