| --- |
| license: apache-2.0 |
| pipeline_tag: image-text-to-text |
| library_name: transformers |
| base_model: |
| - OpenGVLab/InternVL2.5-8B |
| base_model_relation: merge |
| language: |
| - multilingual |
| tags: |
| - Sa2VA |
| - custom_code |
| --- |
| |
| # 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-8B" |
| 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} |
| } |
| ``` |
|
|
