transformers safetensors sa2va_chat feature-extraction Sa2VA custom_code image-text-to-text conversational multilingual arxiv:2501.04001 license:apache-2.0 region:us
---
base_model:
- OpenGVLab/InternVL2.5-4B
language:
- multilingual
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- Sa2VA
- custom_code
base_model_relation: merge
---

# Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

[\[📂 GitHub\]](https://github.com/magic-research/Sa2VA)
[\[📜 Sa2VA paper\]](https://arxiv.org/abs/2501.04001)
[\[🚀 Quick Start\]](#quick-start) 



## Introduction

Sa2VA is an MLLM capable of question answering, visual prompt understanding, and dense object segmentation at both image and video levels. It achieves comparable performance to SOTA MLLMs Qwen2-VL and InternVL2.5 on question-answering benchmarks. Additionally, Sa2VA possesses the visual prompt understanding and dense object segmentation capabilities that SOTA MLLMs Qwen2-VL and InternVL2.5 lack. Sa2VA achieves SOTA performance on both image and video grounding and segmentation benchmarks.

## Sa2VA Family

We built the Sa2VA series based on Qwen2-VL and InternVL2/2.5. In the following table, we provide some Sa2VA models built on InternVL2.5. Other Sa2VA models will be open-sourced soon.

| Model Name |                             Base MLLM                              |                                Language Part                                |                        HF Link                        |
|:----------:|:------------------------------------------------------------------:|:---------------------------------------------------------------------------:|:-----------------------------------------------------:|
|  Sa2VA-1B  | [InternVL2.5-1B](https://huggingface.co/OpenGVLab/InternVL2_5-1B)  | [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)  | [🤗 link](https://huggingface.co/ByteDance/Sa2VA-1B)  |
|  Sa2VA-4B  | [InternVL2.5-4B](https://huggingface.co/OpenGVLab/InternVL2_5-4B)  |   [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)    | [🤗 link](https://huggingface.co/ByteDance/Sa2VA-4B)  |
|  Sa2VA-8B  | [InternVL2.5-8B](https://huggingface.co/OpenGVLab/InternVL2_5-8B)  | [internlm2_5-7b-chat](https://huggingface.co/internlm/internlm2_5-7b-chat)  | [🤗 link](https://huggingface.co/ByteDance/Sa2VA-8B)  |
| Sa2VA-26B  | [InternVL2.5-26B](https://huggingface.co/OpenGVLab/InternVL2_5-26B) | [internlm2_5-20b-chat](https://huggingface.co/internlm/internlm2_5-20b-chat) | [🤗 link](https://huggingface.co/ByteDance/Sa2VA-26B) |

## Sa2VA Performance
| Model Name |   MME    | MMBench  | RefCOCO | RefCOCO+ | RefCOCOg | MeVIS (val_u) | DAVIS |
|:----------:|:--------:|:----:|:-------:|:--------:|:--------:|:-------------:|:-----:|
|  Sa2VA-1B  | 1504/434 | 71.9 |  79.6   |   73.6   |   77.7   |     53.4      | 69.5  |
|  Sa2VA-4B  | 1691/610 | 81.8 |  82.4   |   77.6   |   79.7   |     55.9      | 73.7  |
|  Sa2VA-8B  | 1690/610 | 84.4 |  82.6   |   78.0   |   80.3   |     58.9      | 75.9  |
| Sa2VA-26B | 1698/653 | 85.8 |  82.9   |   79.3   |   81.2   |     61.8      | 78.6  |


## Quick Start

We provide an example code to run `Sa2VA` using `transformers`.

```python
import torch
from transformers import AutoTokenizer, AutoModel
from PIL import Image
import numpy as np
import os

# load the model and tokenizer
path = "ByteDance/Sa2VA-4B"
model = AutoModel.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    use_flash_attn=True,
    trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)

# for image chat
image_path = "/PATH/TO/IMAGE"
text_prompts = "<image>Please describe the image."
image = Image.open(image_path).convert('RGB')
input_dict = {
    'image': image,
    'text': text_prompts,
    'past_text': '',
    'mask_prompts': None,
    'tokenizer': tokenizer,
    }
return_dict = model.predict_forward(**input_dict)
answer = return_dict["prediction"] # the text format answer

# for image chat with segmentation output
image_path = "/PATH/TO/IMAGE"
text_prompts = "<image>Could you please give me a brief description of the image? Please respond with interleaved segmentation masks for the corresponding parts of the answer."
image = Image.open(image_path).convert('RGB')
input_dict = {
    'image': image,
    'text': text_prompts,
    'past_text': '',
    'mask_prompts': None,
    'tokenizer': tokenizer,
    }
return_dict = model.predict_forward(**input_dict)
answer = return_dict["prediction"] # the text format answer
masks = return_dict['prediction_masks']  # segmentation masks, list(np.array(1, h, w), ...)
    
# for chat with visual prompt (mask format) input
mask_prompts = np.load('/PATH/TO/pred_masks.npy') # np.array(n_prompts, h, w)
image_path = "/PATH/TO/IMAGE"
text_prompts = "<image>Can you provide me with a detailed description of the region in the picture marked by region1."
image = Image.open(image_path).convert('RGB')
input_dict = {
    'image': image,
    'text': text_prompts,
    'past_text': '',
    'mask_prompts': mask_prompts,
    'tokenizer': tokenizer,
    }
return_dict = model.predict_forward(**input_dict)
answer = return_dict["prediction"] # the text format answer

# for video chat
video_folder = "/PATH/TO/VIDEO_FOLDER"
images_paths = os.listdir(video_folder)
images_paths = [os.path.join(video_folder, image_path) for image_name in images_paths]
if len(images_paths) > 5:  # uniformly sample 5 frames
    step = (len(images_paths) - 1) // (5 - 1)
    images_paths = [images_paths[0]] + images_paths[1:-1][::step][1:] + [images_paths[-1]]
text_prompts = "<image>Please describe the video."
input_dict = {
    'video': images_paths,
    'text': text_prompts,
    'past_text': '',
    'mask_prompts': None,
    'tokenizer': tokenizer,
}
return_dict = model.predict_forward(**input_dict)
answer = return_dict["prediction"] # the text format answer


# for video chat with segmentation mask output
video_folder = "/PATH/TO/VIDEO_FOLDER"
images_paths = os.listdir(video_folder)
images_paths = [os.path.join(video_folder, image_path) for image_name in images_paths]
text_prompts = "<image>Please segment the person."
input_dict = {
    'video': images_paths,
    'text': text_prompts,
    'past_text': '',
    'mask_prompts': None,
    'tokenizer': tokenizer,
}
return_dict = model.predict_forward(**input_dict)
answer = return_dict["prediction"] # the text format answer
masks = return_dict['prediction_masks']  # segmentation masks, list(np.array(n_frames, h, w), ...)
```

## Citation

If you find this project useful in your research, please consider citing:

```BibTeX
@article{sa2va,
  title={Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos},
  author={Yuan, Haobo and Li, Xiangtai and Zhang, Tao and Huang, Zilong Huang and Xu, Shilin and Ji, Shunping and Tong, Yunhai and Qi, Lu and Feng, Jiashi and Yang, Ming-Hsuan},
  journal={arXiv preprint},
  year={2025}
}
```
