metadata
license: cc-by-nc-sa-4.0
datasets:
- lmms-lab/LLaVA-Video-178K
language:
- en
metrics:
- accuracy
base_model:
- lmms-lab/LLaVA-Video-7B-Qwen2
pipeline_tag: video-text-to-text
library_name: transformers
tags:
- Action
- Video
- MQA
- multimodal
- VLM
- LLaVAction
- MLLMs
model-index:
- name: LLaVAction-7B
results:
- task:
type: multimodal
dataset:
name: EgoSchema
type: egoschema
metrics:
- type: accuracy
value: 59
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: MVBench
type: mvbench
metrics:
- type: accuracy
value: 61.1
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: NextQA
type: nextqa
metrics:
- type: accuracy
value: 82.8
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: PercepTest
type: percepTest
metrics:
- type: accuracy
value: 70.2
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: LongVideoBench
type: longvideobench
metrics:
- type: accuracy
value: 58.6
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: VideoMME
type: videomme
metrics:
- type: accuracy
value: 63.9
name: accuracy
verified: true
- task:
type: multimodal
dataset:
name: VideoMME (w-subs)
type: videomme
metrics:
- type: accuracy
value: 71.4
name: accuracy
verified: true
LLaVAction-7B
LLaVAction: evaluating and training multi-modal large language models for action recognition
Shaokai Ye1** Haozhe Qi1**
Alexander Mathis1† Mackenzie Weygandt Mathis1†‡
1 EPFL
** First authors † Senior Authors ‡ Corresponding Author
Model Summary
The LLaVAction-7B model is trained on EPIC-KITCHENS-100-MQA, based on Qwen2 language model with a context window of 32K tokens. This model supports at most 64 frames.
- Project Page: https://mmathislab.github.io/llavaction/
- Paper: For more details, please check our paper
- Repository: https://github.com/AdaptiveMotorControlLab/LLaVAction
- Point of Contact: Mackenzie Mathis
- Languages: English
Useage
Intended use
The model was trained on EPIC-KITCHENS-100-MQA [dataset release pending] and LLaVA-Video-178K. It has improved capability on understanding human egocentric actions from videos.
Generation
We provide the simple generation process for using our model. For more details, you could refer to our Github.
!pip install llavaction
from llavaction.model.builder import load_pretrained_model
from llavaction.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from llavaction.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
from llavaction.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")
#Your video (it assumes an egocentric view point)
video_path = "XXXX"
#These are the prompts we trained with, but you can test others:
perspective_prompt = "You are seeing this video from egocentric view and you are the person. Your hands are sometimes interacting with objects. What action are you doing?"
task_prompt = "Describe in details what you see from the video frames."
def load_video(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)]
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]
spare_frames = vr.get_batch(frame_idx).asnumpy()
# import pdb;pdb.set_trace()
return spare_frames,frame_time,video_time
pretrained = "MLAdaptiveIntelligence/LLaVAction-7B"
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()
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().to(torch.bfloat16)
video = [video]
conv_template = "qwen_1_5" # Make sure you use correct chat template for different models
time_instruction = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. "
question = DEFAULT_IMAGE_TOKEN + f"\n{time_instruction}\n{perspective_prompt} {task_prompt}"
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
See details in Ye et al. 2025: arxiv.org/abs/2503.18712
Model
- Architecture: SO400M + Qwen2
- Initialized Model: lmms-lab/LLaVA-Video-7B-Qwen2
- Data: A mixture of LLaVA-178K and EPIC-KITCHENS-100-MQA, 2 epochs, full model
- Precision: bfloat16
Hardware & Software
GPUs: 32 * Nvidia GH-200 (for whole model series training) Orchestration: HuggingFace Trainer Neural networks: PyTorch
Citation
arXiv: arxiv.org/abs/2503.18712
@article{YeQi2025llavaction,
title={LLaVAction: evaluating and training multi-modal large language models for action recognition},
author={Ye, Shaokai and Qi, Haozhe and Mathis, Alexander and Mathis, Mackenzie W.},
journal={arXiv preprint},
year={2025}
}