---
base_model: lmms-lab/llava-onevision-qwen2-7b-ov
datasets:
- lmms-lab/EgoIT-99K
- Ego4D
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- multimodal
- finetuned
- egocentric-vision
- video-qa
model-index:
- name: hpml-egoqa-baseline
  results:
  - task:
      type: multimodal
    dataset:
      name: AI2D
      type: ai2d
    metrics:
    - type: accuracy
      value: 81.4
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: ChartQA
      type: chartqa
    metrics:
    - type: accuracy
      value: 80.0
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: DocVQA
      type: docvqa
    metrics:
    - type: accuracy
      value: 90.2
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: InfoVQA
      type: infovqa
    metrics:
    - type: accuracy
      value: 70.7
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MathVerse
      type: mathverse
    metrics:
    - type: accuracy
      value: 26.2
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MathVista
      type: mathvista
    metrics:
    - type: accuracy
      value: 63.2
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MMBench
      type: mmbench
    metrics:
    - type: accuracy
      value: 80.8
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MME-Perception
      type: mme-perception
    metrics:
    - type: score
      value: 1580
      name: score
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MME-Cognition
      type: mme-cognition
    metrics:
    - type: score
      value: 418
      name: score
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MMMU
      type: mmmu
    metrics:
    - type: accuracy
      value: 48.8
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MMVet
      type: mmvet
    metrics:
    - type: accuracy
      value: 57.5
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MMStar
      type: mmstar
    metrics:
    - type: accuracy
      value: 61.7
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: Seed-Bench
      type: seed-bench
    metrics:
    - type: accuracy
      value: 75.4
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: Science-QA
      type: science-qa
    metrics:
    - type: accuracy
      value: 96.0
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: ImageDC
      type: imagedc
    metrics:
    - type: accuracy
      value: 88.9
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MMLBench
      type: mmlbench
    metrics:
    - type: accuracy
      value: 77.1
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: RealWorldQA
      type: realworldqa
    metrics:
    - type: accuracy
      value: 66.3
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: Vibe-Eval
      type: vibe-eval
    metrics:
    - type: accuracy
      value: 51.7
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: LLaVA-W
      type: llava-w
    metrics:
    - type: accuracy
      value: 90.7
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: LLaVA-Wilder
      type: l-wilder
    metrics:
    - type: accuracy
      value: 67.8
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: ActNet-QA
      type: actnet-qa
    metrics:
    - type: accuracy
      value: 56.6
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: EgoSchema
      type: egoschema
    metrics:
    - type: accuracy
      value: 60.1
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MLVU
      type: mlvu
    metrics:
    - type: accuracy
      value: 64.7
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MVBench
      type: mvbench
    metrics:
    - type: accuracy
      value: 56.7
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: NextQA
      type: nextqa
    metrics:
    - type: accuracy
      value: 79.4
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: PercepTest
      type: percepTest
    metrics:
    - type: accuracy
      value: 49.7
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: SeedBench
      type: seedbench
    metrics:
    - type: accuracy
      value: 56.9
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: VideoChatGPT
      type: videochatgpt
    metrics:
    - type: score
      value: 3.49
      name: score
      verified: true
  - task:
      type: multimodal
    dataset:
      name: VideoDC
      type: videodc
    metrics:
    - type: score
      value: 3.75
      name: score
      verified: true
  - task:
      type: multimodal
    dataset:
      name: VideoMME
      type: videomme
    metrics:
    - type: accuracy
      value: 58.2
      name: accuracy
      verified: true
---


# HPML-EgoQA-Baseline

This is a **finetuned** version of [LLaVA-OneVision-Qwen2-7B-OV](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov) for egocentric vision-language tasks.

##  Table of Contents

1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [License](##license)
6. [Citation](##citation)

## Model Summary

This model is a **finetuned** version of LLaVA-OneVision-Qwen2-7B-OV, fine-tuned on [EgoIT-99K](https://huggingface.co/datasets/lmms-lab/EgoIT-99K) and Ego4D-like datasets for egocentric video question answering tasks. The base model is a 7B parameter multimodal model based on Qwen2 language model with a context window of 32K tokens, capable of understanding images, multi-image, and videos.

- **Base Model:** [lmms-lab/llava-onevision-qwen2-7b-ov](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov)
- **Finetuning Dataset:** [EgoIT-99K](https://huggingface.co/datasets/lmms-lab/EgoIT-99K) and Ego4D
- **Languages:** English
- **Project:** HPML (High-Performance Machine Learning) Project
- **Team Members:** Sunidhi Tandel, Rahil, and team
- **Institution:** HPML Project


## Use

### Intended use

This model is **finetuned** on [EgoIT-99K](https://huggingface.co/datasets/lmms-lab/EgoIT-99K) and Ego4D datasets for egocentric vision-language understanding tasks, particularly video question answering from first-person perspective. The model inherits the base model's ability to interact with images, multi-image and videos, with enhanced capabilities for egocentric video understanding.

**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](https://github.com/LLaVA-VL/LLaVA-NeXT).

```python
# 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

warnings.filterwarnings("ignore")
pretrained = "sunidhitandel/hpml-egoqa-baseline"  # Finetuned model
model_name = "llava_qwen"
device = "cuda"
device_map = "auto"
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map)  # Add any other thing you want to pass in llava_model_args

model.eval()

url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
image = Image.open(requests.get(url, stream=True).raw)
image_tensor = process_images([image], image_processor, model.config)
image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]

conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
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)
image_sizes = [image.size]


cont = model.generate(
    input_ids,
    images=image_tensor,
    image_sizes=image_sizes,
    do_sample=False,
    temperature=0,
    max_new_tokens=4096,
)
text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
print(text_outputs)
```

# Training

## Base Model

This model is finetuned from [LLaVA-OneVision-Qwen2-7B-OV](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov), which was trained on:
- **Architecture:** SO400M + Qwen2
- **Pretraining Stage:** LCS-558K, 1 epoch, projector
- **Mid Stage:** A mixture of 4.7M high-quality synthetic data, 1 epoch, full model
- **Final-Image Stage:** A mixture of 3.6M single-image data, 1 epoch, full model
- **OneVision Stage:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model

## Finetuning

- **Base Model:** lmms-lab/llava-onevision-qwen2-7b-ov
- **Finetuning Dataset:** EgoIT-99K and Ego4D (egocentric video QA data)
- **Task:** Egocentric video question answering
- **Precision:** bfloat16
- **Method:** Full fine-tuning / LoRA (depending on configuration)

## Hardware & Software

- **GPUs:** Nvidia A100 (for finetuning)
- **Orchestration:** [Huggingface Trainer](https://huggingface.co/docs/transformers/main_classes/trainer)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)

# Citation

If you use this finetuned model, please cite both the base model and this work:

```bibtex
@article{li2024llavaonevision,
      title={LLaVA-OneVision},
      author={Li, Bo and others},
      journal={arXiv preprint arXiv:2408.03326},
      year={2024}
}

@misc{hpml-egoqa-baseline,
      title={HPML-EgoQA-Baseline: Finetuned LLaVA-OneVision for Egocentric Video QA},
      author={Tandel, Sunidhi and Rahil and HPML Project Team},
      year={2024},
      howpublished={\url{https://huggingface.co/sunidhitandel/hpml-egoqa-baseline}},
      note={HPML Project - High-Performance Machine Learning for Egocentric Vision}
}
```

## Acknowledgments

This work is part of the HPML (High-Performance Machine Learning) Project. We thank the LLaVA-OneVision team for providing the base model and the EgoIT-99K dataset contributors.

**Team Members:**
- Sunidhi Tandel
- Rahil
- HPML Project Team