File size: 8,874 Bytes
c6c0583 aeb9591 c6c0583 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
---
license: mit
pipeline_tag: image-text-to-text
library_name: transformers
base_model:
- OpenGVLab/InternVL2_5-8B
- OpenGVLab/InternVL2_5-8B-MPO
base_model_relation: finetune
datasets:
- OpenGVLab/MMPR-v1.2
- OpenGVLab/VisualPRM400K-v1.1
language:
- multilingual
tags:
- internvl
- custom_code
---
# VisualPRM-8B-v1.1
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL)
[\[📜 Paper\]](https://arxiv.org/abs/2503.10291)
[\[🆕 Blog\]](https://internvl.github.io/blog/2025-03-13-VisualPRM/)
[\[🤗 model\]](https://huggingface.co/OpenGVLab/VisualPRM-8B-v1.1)
[\[🤗 dataset\]](https://huggingface.co/datasets/OpenGVLab/VisualPRM400K)
[\[🤗 benchmark\]](https://huggingface.co/datasets/OpenGVLab/VisualProcessBench)
***This is a newer version of [VisualPRM-8B](https://huggingface.co/OpenGVLab/VisualPRM-8B), which exhibits superior performance compared to [VisualPRM-8B](https://huggingface.co/OpenGVLab/VisualPRM-8B). Using VisualPRM-8B-v1.1 as the critic model, the reasoning abilities of InternVL3 are further enhanced.***
## Introduction
We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM) with 8B parameters, which improves the reasoning abilities of existing Multimodal Large Language Models (MLLMs) across different model scales and families with Best-of-N (BoN) evaluation strategies. **Specifically, our model improves the reasoning performance of three types of MLLMs and four different model scales. Even when applied to the highly capable InternVL2.5-78B, it achieves a 5.9-point improvement across seven multimodal reasoning benchmarks.** Experimental results show that our model exhibits superior performance compared to Outcome Reward Models and Self-Consistency during BoN evaluation. To facilitate the training of multimodal PRMs, we construct a multimodal process supervision dataset VisualPRM400K using an automated data pipeline. For the evaluation of multimodal PRMs, we propose VisualProcessBench, a benchmark with human-annotated step-wise correctness labels, to measure the abilities of PRMs to detect erroneous steps in multimodal reasoning tasks. We hope that our work can inspire more future research and contribute to the development of MLLMs.
## Performance
## Inference with Transformers
```python
import torch
import torchvision.transforms as T
from PIL import Image
from transformers import AutoModel, AutoTokenizer
from torchvision.transforms.functional import InterpolationMode
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(input_size):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def load_image(image, input_size=448, max_num=12):
image = Image.open(image).convert('RGB')
transform = build_transform(input_size=input_size)
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
def main():
path = 'OpenGVLab/VisualPRM-8B-v1.1'
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
model = AutoModel.from_pretrained(
path,
trust_remote_code=True,
low_cpu_mem_usage=True,
torch_dtype=torch.bfloat16,
).eval().cuda()
question = 'Find m \\angle 3.'
response_a = """
To find the measure of \\angle 3, we need to use the properties of the angles in the given geometric figure.
1. \\angle EDC is a right angle, and it is split into \\angle EDF and \\angle 3.
2. We know that \\angle EDF = 39^\\circ because it is labeled in the diagram.
3. Since \\angle EDC is 90^\\circ, we can set up the equation for \\angle 3: \\angle EDC = \\angle EDF + \\angle 3
4. Substituting the known values, we get: 90^\\circ = 39^\\circ + \\angle 3
5. Solving for \\angle 3: \\angle 3 = 90^\\circ - 39^\\circ
6. Simplifying the equation: \\angle 3 = 51^\\circ
Final answer: 51^\\circ
""".strip()
response_b = """
To find \\( m \\angle 3 \\), we need to analyze the given geometric figure carefully.
1. Recognize that \\( \\angle 3 \\) is part of triangle \\( BCE \\). Since \\( \\angle 1 \\) is given as 48° and \\( \\angle 2 \\) is a right angle (90°), we can use the properties of a right triangle to find \\( \\angle 3 \\).
2. The sum of the angles in any triangle is 180°. Therefore, in triangle \\( BCE \\): \[ \\angle 1 + \\angle 2 + \\angle 3 = 180° \]
3. Substitute the known values: \[ 48° + 90° + \\angle 3 = 180° \]
4. Solve for \\( \\angle 3 \\): \[ \\angle 3 = 180° - 48° - 90° = 42° \]
Thus, the measure of \\( \\angle 3 \\) is 42°.
Final answer: 42
""".strip()
response_list = [
response_a,
response_b,
]
image = 'examples/image.png'
pixel_values = load_image(image).to(torch.bfloat16).cuda()
sorted_response_list = model.select_best_response(
tokenizer=tokenizer,
question=question,
response_list=response_list,
pixel_values=pixel_values,
return_scores=True,
)
print('Best response:', sorted_response_list[0][0])
print('Highest score:', sorted_response_list[0][1])
if __name__ == '__main__':
main()
```
## License
This project is released under the MIT License. This project uses the pre-trained internlm2_5-7b-chat as a component, which is licensed under the Apache License 2.0.
## Citation
If you find this project useful in your research, please consider citing:
```BibTeX
@article{wang2025visualprm,
title={VisualPRM: An Effective Process Reward Model for Multimodal Reasoning},
author={Wang, Weiyun and Gao, Zhangwei and Chen, Lianjie and Chen, Zhe and Zhu, Jinguo and Zhao, Xiangyu and Liu, Yangzhou and Cao, Yue and Ye, Shenglong and Zhu, Xizhou and others},
journal={arXiv preprint arXiv:2503.10291},
year={2025}
}
```
|