metadata
datasets:
- lmms-lab/LLaVA-OneVision-Data
- lmms-lab/LLaVA-Video-178K
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- multimodal
model-index:
- name: LLaVA-Video-7B-Qwen2
results:
- task:
type: multimodal
dataset:
name: ActNet-QA
type: actnet-qa
metrics:
- type: accuracy
value: 56.5
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: EgoSchema
type: egoschema
metrics:
- type: accuracy
value: 57.3
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: MLVU
type: mlvu
metrics:
- type: accuracy
value: 70.8
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: MVBench
type: mvbench
metrics:
- type: accuracy
value: 58.6
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: NextQA
type: nextqa
metrics:
- type: accuracy
value: 83.2
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: PercepTest
type: percepTest
metrics:
- type: accuracy
value: 67.9
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: VideoChatGPT
type: videochatgpt
metrics:
- type: score
value: 3.52
name: score
verified: true
- task:
type: multimodal
dataset:
name: VideoDC
type: videodc
metrics:
- type: score
value: 3.66
name: score
verified: true
- task:
type: multimodal
dataset:
name: LongVideoBench
type: longvideobench
metrics:
- type: accuracy
value: 58.2
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: VideoMME
type: videomme
metrics:
- type: accuracy
value: 63.3
name: accuracy
verified: true
base_model:
- lmms-lab/llava-onevision-qwen2-7b-si
LLaVA-Video-7B-Qwen2
Table of Contents
Model Summary
The LLaVA-Video models are 7/72B parameter models trained on LLaVA-Video-178K and LLaVA-OneVision Dataset, based on Qwen2 language model with a context window of 32K tokens.
This model support at most 64 frames.
- Project Page: Project Page.
- Paper: For more details, please check our paper
- Repository: LLaVA-VL/LLaVA-NeXT
- Point of Contact: Yuanhan Zhang
- Languages: English, Chinese
Use
Intended use
The model was trained on LLaVA-Video-178K and LLaVA-OneVision Dataset, having the ability to interact with images, multi-image and videos, but specific to videos.
Feel free to share your generations in the Community tab!
Generation
We provide the simple generation process for using our model. For more details, you could refer to Github.
# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
from llava.conversation import conv_templates, SeparatorStyle
from PIL import Image
import requests
import copy
import torch
import sys
import warnings
from decord import VideoReader, cpu
import numpy as np
warnings.filterwarnings("ignore")
def load_video(self, video_path, max_frames_num,fps=1,force_sample=False):
if max_frames_num == 0:
return np.zeros((1, 336, 336, 3))
vr = VideoReader(video_path, ctx=cpu(0),num_threads=1)
total_frame_num = len(vr)
video_time = total_frame_num / vr.get_avg_fps()
fps = round(vr.get_avg_fps()/fps)
frame_idx = [i for i in range(0, len(vr), fps)]
frame_time = [i/fps for i in frame_idx]
if len(frame_idx) > max_frames_num or force_sample:
sample_fps = max_frames_num
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
frame_time = [i/vr.get_avg_fps() for i in frame_idx]
frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
spare_frames = vr.get_batch(frame_idx).asnumpy()
# import pdb;pdb.set_trace()
return spare_frames,frame_time,video_time
pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2"
model_name = "llava_qwen"
device = "cuda"
device_map = "auto"
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map) # Add any other thing you want to pass in llava_model_args
model.eval()
video_path = "XXXX"
max_frames_num = "64"
video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sample=True)
video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().bfloat16()
video = [video]
conv_template = "qwen_1_5" # Make sure you use correct chat template for different models
time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}.Please answer the following questions related to this video."
question = DEFAULT_IMAGE_TOKEN + f"{time_instruciton}\nPlease describe this video in detail."
conv = copy.deepcopy(conv_templates[conv_template])
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()
input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
cont = model.generate(
input_ids,
images=video,
modalities= ["video"],
do_sample=False,
temperature=0,
max_new_tokens=4096,
)
text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)[0].strip()
print(text_outputs)
Training
Model
- Architecture: SO400M + Qwen2
- Initialized Model: lmms-lab/llava-onevision-qwen2-7b-si
- Data: A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
- Precision: bfloat16
Hardware & Software
- GPUs: 256 * Nvidia Tesla A100 (for whole model series training)
- Orchestration: Huggingface Trainer
- Neural networks: PyTorch
Citation
@misc{zhang2024videoinstructiontuningsynthetic, title={Video Instruction Tuning With Synthetic Data}, author={Yuanhan Zhang and Jinming Wu and Wei Li and Bo Li and Zejun Ma and Ziwei Liu and Chunyuan Li}, year={2024}, eprint={2410.02713}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2410.02713}, }