---
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
license: apache-2.0
tags:
- vision-language
- cinematography
- shotbench
pipeline_tag: image-text-to-text
library_name: transformers
---

## Model description

This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), introduced in the paper [ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models](https://huggingface.co/papers/2506.21356). It is trained on the largest and high-quality dataset for cinematic language understanding to date. It currently achieves state-of-the-art performance on [ShotBench](https://vchitect.github.io/ShotBench-project/), a comprehensive benchmark for evaluating cinematography understanding in vision-language models.

**Project Page:** [https://vchitect.github.io/ShotBench-project/](https://vchitect.github.io/ShotBench-project/)

**Code:** [https://github.com/Vchitect/ShotBench](https://github.com/Vchitect/ShotBench)

### Demo

**Image**

```python
import cv2
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

device = "cuda"
device_map = "balanced"
dtype = torch.bfloat16
image_path = "/path/to/image.jpg"

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
  "Vchitect/ShotVL-7B",
  device_map=device_map,
  attn_implementation="flash_attention_2",
  torch_dtype=dtype,
).eval()
processor = AutoProcessor.from_pretrained(
  "Vchitect/ShotVL-7B", revision="refs/pr/24", use_fast=True, torch_dtype=dtype
)

msgs = [
  {"role": "system", "content": "You are a helpful assistant."},
  {
    "role": "user",
    "content": [
      {"type": "image", "image": image_path},
      {"type": "text", "text": "What's the shot size of this shot?"},
    ],
  },
]

text = processor.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(msgs)
inputs = processor(
  text=[text],
  images=image_inputs,
  videos=video_inputs,
  padding=True,
  return_tensors="pt",
).to(device)

with torch.inference_mode():
  out_ids = model.generate(**inputs, max_new_tokens=640)
  
trimmed = [o[len(i):] for i, o in zip(inputs.input_ids, out_ids)]
print(processor.batch_decode(trimmed, skip_special_tokens=True)[0])
```

**Video**

```python
import cv2
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

device = "cuda"
device_map = "balanced"
dtype = torch.bfloat16
video_path = "/path/to/video.mp4"

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
  "Vchitect/ShotVL-7B",
  device_map=device_map,
  attn_implementation="flash_attention_2",
  torch_dtype=dtype,
).eval()
processor = AutoProcessor.from_pretrained(
  "Vchitect/ShotVL-7B", revision="refs/pr/24", use_fast=True, torch_dtype=dtype
)

question = (
    "What's the camera movement in this movie shot?
"
    "Options:
A. Boom down
B. Boom up
C. Push in
D. Pull out
"
    "Please select the most likely answer from the options above.
"
)
msgs = [
  {"role": "system", "content": "You are a helpful assistant."},
  {
    "role": "user",
    "content": [
      {"type": "video", "video": video_path, "max_pixels": 360*640, "fps": 12.0},
      {"type": "text", "text": question},
    ],
  },
]

text = processor.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(msgs)
inputs = processor(
  text=[text],
  images=image_inputs,
  videos=video_inputs,
  padding=True,
  return_tensors="pt",
).to(device)

with torch.inference_mode():
  out_ids = model.generate(**inputs, max_new_tokens=640)
  
trimmed = [o[len(i):] for i, o in zip(inputs.input_ids, out_ids)]
print(processor.batch_decode(trimmed, skip_special_tokens=True)[0])
```

## Evaluation Results

<div align="center">
<table>
  <caption>
    <small>
      Abbreviations:&nbsp;
      SS = <em>Shot&nbsp;Size</em>,&nbsp;
      SF = <em>Shot&nbsp;Framing</em>,&nbsp;
      CA = <em>Camera&nbsp;Angle</em>,&nbsp;
      LS = <em>Lens&nbsp;Size</em>,&nbsp;
      LT = <em>Lighting&nbsp;Type</em>,&nbsp;
      LC = <em>Lighting&nbsp;Conditions</em>,&nbsp;
      SC = <em>Shot&nbsp;Composition</em>,&nbsp;
      CM = <em>Camera&nbsp;Movement</em>.&nbsp;
      <u>Underline</u> marks previous best in each group.<br>
      <strong>Our <em>ShotVL</em> models establish new SOTA.</strong>
    </small>
  </caption><thead>
    <tr>
      <th>Models</th><th>SS</th><th>SF</th><th>CA</th><th>LS</th><th>LT</th>
      <th>LC</th><th>SC</th><th>CM</th><th>Avg</th>
    </tr>
  </thead><tbody>
  <tr><th colspan="10"><em>Open-Sourced&nbsp;VLMs</em></th></tr>
                            <tr><td>Qwen2.5-VL-3B-Instruct</td><td>54.6</td><td>56.6</td><td>43.1</td><td>36.6</td><td>59.3</td><td>45.1</td><td>41.5</td><td>31.9</td><td>46.1</td></tr>
                            <tr><td>Qwen2.5-VL-7B-Instruct</td><td>69.1</td><td>73.5</td><td>53.2</td><td>47.0</td><td>60.5</td><td>47.4</td><td>49.9</td><td>30.2</td><td>53.8</td></tr>
                            <tr><td>LLaVA-NeXT-Video-7B</td><td>35.9</td><td>37.1</td><td>32.5</td><td>27.8</td><td>50.9</td><td>31.7</td><td>28.0</td><td>31.3</td><td>34.4</td></tr>
                            <tr><td>LLaVA-Video-7B-Qwen2</td><td>56.9</td><td>65.4</td><td>45.1</td><td>36.0</td><td>63.5</td><td>45.4</td><td>37.4</td><td>35.3</td><td>48.1</td></tr>
                            <tr><td>LLaVA-Onevision-Qwen2-7B-Ov-Chat</td><td>58.4</td><td>71.0</td><td>52.3</td><td>38.7</td><td>59.5</td><td>44.9</td><td>50.9</td><td>39.7</td><td>51.9</td></tr>
                            <tr><td>InternVL2.5-8B</td><td>56.3</td><td>70.3</td><td>50.8</td><td>41.1</td><td>60.2</td><td>45.1</td><td>50.1</td><td>33.6</td><td>50.9</td></tr>
                            <tr><td>InternVL3-2B</td><td>56.3</td><td>56.0</td><td>44.4</td><td>34.6</td><td>56.8</td><td>44.6</td><td>43.0</td><td>38.1</td><td>46.7</td></tr>
                            <tr><td>InternVL3-8B</td><td>62.1</td><td>65.8</td><td>46.8</td><td>42.9</td><td>58.0</td><td>44.3</td><td>46.8</td><td>44.2</td><td>51.4</td></tr>
                            <tr><td>InternVL3-14B</td><td>59.6</td><td>82.2</td><td>55.4</td><td>40.7</td><td>61.7</td><td>44.6</td><td>51.1</td><td>38.2</td><td>54.2</td></tr>
                            <tr><td>Internlm-xcomposer2d5-7B</td><td>51.1</td><td>71.0</td><td>39.8</td><td>32.7</td><td>59.3</td><td>35.7</td><td>35.7</td><td>38.8</td><td>45.5</td></tr>
                            <tr><td>Ovis2-8B</td><td>35.9</td><td>37.1</td><td>32.5</td><td>27.8</td><td>50.9</td><td>31.7</td><td>28.0</td><td>35.3</td><td>34.9</td></tr>
                            <tr><td>VILA1.5-3B</td><td>33.4</td><td>44.9</td><td>32.1</td><td>28.6</td><td>50.6</td><td>35.7</td><td>28.4</td><td>21.5</td><td>34.4</td></tr>
                            <tr><td>VILA1.5-8B</td><td>40.6</td><td>44.5</td><td>39.1</td><td>29.7</td><td>48.9</td><td>32.9</td><td>34.4</td><td>36.9</td><td>38.4</td></tr>
                            <tr><td>VILA1.5-13B</td><td>36.7</td><td>54.6</td><td>40.7</td><td>34.8</td><td>52.8</td><td>35.4</td><td>34.2</td><td>31.3</td><td>40.1</td></tr>
                            <tr><td>Instructblip-vicuna-7B</td><td>27.0</td><td>27.9</td><td>34.5</td><td>29.4</td><td>44.4</td><td>29.7</td><td>27.1</td><td>25.0</td><td>30.6</td></tr>
                            <tr><td>Instructblip-vicuna-13B</td><td>26.8</td><td>29.2</td><td>27.9</td><td>28.0</td><td>39.0</td><td>24.0</td><td>27.1</td><td>22.0</td><td>28.0</td></tr>
                            <tr><td>InternVL2.5-38B</td><td>67.8</td><td><u>85.4</u></td><td>55.4</td><td>41.7</td><td>61.7</td><td>48.9</td><td>52.4</td><td>44.0</td><td>57.2</td></tr>
                            <tr><td>InternVL3-38B</td><td>68.0</td><td>84.0</td><td>51.9</td><td>43.6</td><td>64.4</td><td>46.9</td><td>54.7</td><td>44.6</td><td>57.3</td></tr>
                            <tr><td>Qwen2.5-VL-32B-Instruct</td><td>62.3</td><td>76.6</td><td>51.0</td><td>48.3</td><td>61.7</td><td>44.0</td><td>52.2</td><td>43.8</td><td>55.0</td></tr>
                            <tr><td>Qwen2.5-VL-72B-Instruct</td><td><u>75.1</u></td><td>82.9</td><td>56.7</td><td>46.8</td><td>59.0</td><td><u>49.4</u></td><td>54.1</td><td><u>48.9</u></td><td>59.1</td></tr>
                            <tr><td>InternVL3-78B</td><td>69.7</td><td>80.0</td><td>54.5</td><td>44.0</td><td><u>65.5</u></td><td>47.4</td><td>51.8</td><td>44.4</td><td>57.2</td></tr>
<tr><th colspan="10"><em>Proprietary&nbsp;VLMs</em></th></tr>
                            <tr><td>Gemini-2.0-flash</td><td>48.9</td><td>75.5</td><td>44.6</td><td>31.9</td><td>62.2</td><td>48.9</td><td>52.4</td><td>47.4</td><td>51.5</td></tr>
                            <tr><td>Gemini-2.5-flash-preview-04-17</td><td>57.7</td><td>82.9</td><td>51.4</td><td>43.8</td><td>65.2</td><td>45.7</td><td>45.9</td><td>43.5</td><td>54.5</td></tr>
                            <tr><td>GPT-4o</td><td>69.3</td><td>83.1</td><td><u>58.2</u></td><td><u>48.9</u></td><td>63.2</td><td>48.0</td><td><u>55.2</u></td><td>48.3</td><td><u>59.3</u></td></tr>
<tr><th colspan="10"><em>Ours</em></th></tr>
<tr>
  <td>ShotVL-3B
    <a href="https://huggingface.co/Vchitect/ShotVL-3B">
      <img src="https://img.shields.io/badge/Model-HF-yellow?logo=huggingface" alt="HF">
    </a>
  </td>
  <td>77.9</td><td>85.6</td><td>68.8</td><td>59.3</td><td>65.7</td>
  <td>53.1</td><td>57.4</td><td>51.7</td><td>65.1</td>
</tr>
<tr>
  <td>ShotVL-7B
    <a href="https://huggingface.co/Vchitect/ShotVL-7B">
      <img src="https://img.shields.io/badge/Model-HF-yellow?logo=huggingface" alt="HF">
    </a>
  </td>
  <td>81.2</td><td>90.1</td><td>78.0</td><td>68.5</td><td>70.1</td>
  <td>64.3</td><td>45.7</td><td>62.9</td><td>70.1</td>
</tr>  </tbody>
</table></div>

## BibTeX

```
@misc{
      liu2025shotbench,
      title={ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models}, 
      author={Hongbo Liu and Jingwen He and Yi Jin and Dian Zheng and Yuhao Dong and Fan Zhang and Ziqi Huang and Yinan He and Yangguang Li and Weichao Chen and Yu Qiao and Wanli Ouyang and Shengjie Zhao and Ziwei Liu},
      year={2025},
      eprint={2506.21356},
      achivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.21356}, 
    }
```