VARCO-VISION-14B-HF / README.md
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metadata
language:
  - en
  - ko
license: cc-by-nc-4.0
tags:
  - multimodal
  - conversational
  - ncsoft
  - varco
base_model:
  - Qwen/Qwen2.5-14B-Instruct
  - google/siglip-so400m-patch14-384
library_name: transformers
pipeline_tag: image-text-to-text

VARCO-VISION-14B-HF

About the Model

VARCO-VISION-14B is a powerful English-Korean Vision-Language Model (VLM). The training pipeline of VARCO-VISION consists of four stages: Feature Alignment Pre-training, Basic Supervised Fine-tuning, Advanced Supervised Fine-tuning, and Preference Optimization. In both multimodal and text-only benchmarks, VARCO-VISION-14B not only surpasses other models of similar size in performance but also achieves scores comparable to those of proprietary models. The model currently accepts a single image and a text as inputs, generating an output text. It supports grounding, referring as well as OCR (Optical Character Recognition).

Uses

Direct Use

To use this model, ensure you have transformers >= 4.45.0 installed.

import torch
import requests
from PIL import Image
from transformers import LlavaOnevisionForConditionalGeneration, AutoProcessor

model_name = "NCSOFT/VARCO-VISION-14B-HF"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
        model_name,
        torch_dtype="float16",
        device_map="auto",
        attn_implementation="flash_attention_2"
    )
processor = AutoProcessor.from_pretrained(model_name)
device = model.device

# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image")
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this image."},
            {"type": "image"},
        ],
    },
]

prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)

EOS_TOKEN = "<|im_end|>"
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(device, torch.float16)

output = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
output = processor.decode(output[0][inputs.input_ids.shape[1]:])
if output.endswith(EOS_TOKEN):
    output = output[: -len(EOS_TOKEN)]

output = output.strip()
print(output)

Specialized Features

If a question is based on bounding boxes or require bounding boxes as an output, please include the special tokens in the input text.

The following special tokens are used to define specific tasks, inputs, and outputs for the model:

  • <gro>: Indicates that the model's response should include bounding box information.
  • <ocr>: Specifies OCR tasks for recognizing text within an image.
  • <char> and </char>: Used to mark a text phrase.
  • <obj> and </obj>: Used to indicate an object.
  • <bbox> and </bbox>: Used to represent a bounding box.
  • <delim>: Represents multiple location points for a single object or text.

Grounding

Grounding refers to a task where the model needs to identify specific locations within an image to provide an appropriate answer. To perform grounding, prepend the special token <gro> to the question.

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "<gro>\nDescribe the image in detail."},
            {"type": "image"},
        ],
    },
]

Expected Output Example:

The image shows <obj>two cats</obj><bbox>0.014, 0.106, 0.51, 0.996<delim>0.51, 0.054, 0.996, 0.787</bbox> lying on <obj>a pink blanket</obj><bbox>0.003, 0.231, 0.999, 0.999</bbox>. The cat on the left is lying on its side with its head resting on the blanket, while the cat on the right is lying on its stomach with its head also resting on the blanket. Both cats appear to be relaxed and comfortable. There are <obj>two remote controls</obj><bbox>0.037, 0.141, 0.283, 0.253<delim>0.506, 0.171, 0.581, 0.295</bbox> placed near the cats, one on the left side and one on the right side of the image.
Grounding Example

Referring

VARCO-VISION-14B can handle location-specific questions using bounding boxes. To perform referring tasks, make a conversation including the object of interest within <obj> and </obj> tags. You have to specify its location with <bbox> and </bbox> tags. This allows the model to understand the context and focus on the object at the specified location. A bbox is represented in a form of (x1, y1, x2, y2). The first two values indicate the top-left position of a bbox, and the latter two values are the bottom-right position.

conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "<obj>์ด ๋ฌผ๊ฑด</obj><bbox>0.039, 0.138, 0.283, 0.257</bbox>์€ ์–ด๋–ป๊ฒŒ ์“ฐ๋Š”๊ฑฐ์•ผ?",
            },
            {"type": "image"},
        ],
    },
]

Expected Output Example:

**์ด ๋ฌผ๊ฑด**์€ ๋ฆฌ๋ชจ์ปจ์œผ๋กœ, ์ฃผ๋กœ ํ…”๋ ˆ๋น„์ „์ด๋‚˜ ๋‹ค๋ฅธ ์ „์ž ๊ธฐ๊ธฐ๋ฅผ ์›๊ฒฉ์œผ๋กœ ์กฐ์ž‘ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋ฉ๋‹ˆ๋‹ค. ๋ฆฌ๋ชจ์ปจ์—๋Š” ๋‹ค์–‘ํ•œ ๋ฒ„ํŠผ์ด ์žˆ์œผ๋ฉฐ, ๊ฐ  ๋ฒ„ํŠผ์€ ์ฑ„๋„ ๋ณ€๊ฒฝ, ๋ณผ๋ฅจ ์กฐ์ ˆ, ์ „์› ์ผœ๊ธฐ/๋„๊ธฐ ๋“ฑ์˜ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค. ์‚ฌ์šฉ์ž๋Š” ๋ฆฌ๋ชจ์ปจ์„ ์†์— ๋“ค๊ณ  ๋ฒ„ํŠผ์„ ๋ˆ„๋ฅด๋ฉด, ํ•ด๋‹น ๊ธฐ๊ธฐ์— ์‹ ํ˜ธ๋ฅผ ๋ณด๋‚ด ์›ํ•˜๋Š” ์กฐ์ž‘์„ ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ฆฌ๋ชจ์ปจ์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๊ฐ€์ •์ด๋‚˜ ์‚ฌ๋ฌด์‹ค์—์„œ ํŽธ๋ฆฌํ•˜๊ฒŒ ์ „์ž ๊ธฐ๊ธฐ๋ฅผ ์กฐ์ž‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ์‚ฌ์šฉ๋ฉ๋‹ˆ๋‹ค.

OCR

To perform Optical Character Recognition (OCR), use the <ocr> token.

image_file = "./assets/ocr_1.png"
raw_image = Image.open(image_file)

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "<ocr>"},
            {"type": "image"},
        ],
    },
]

Expected Output Example:

<char>๋ฐฑ๋ฒ”๋กœ</char><bbox>0.172, 0.266, 0.328, 0.341</bbox>
<char>124๋ฒˆ๊ธธ</char><bbox>0.347, 0.266, 0.512, 0.341</bbox>
<char>Baekbeom-ro</char><bbox>0.171, 0.337, 0.433, 0.392</bbox>
<char>124</char><bbox>0.444, 0.341, 0.508, 0.392</bbox>
<char>๋งŒ์ˆ˜์ฃผ๊ณต์•„ํŒŒํŠธ</char><bbox>0.109, 0.531, 0.335, 0.601</bbox>
<char>์‹œํฅ</char><bbox>0.443, 0.518, 0.522, 0.581</bbox>
<char>์‹œ์ฒญ</char><bbox>0.711, 0.521, 0.811, 0.594</bbox>
<char>Mansu</char><bbox>0.102, 0.601, 0.181, 0.648</bbox>
<char>Jugong</char><bbox>0.186, 0.601, 0.273, 0.658</bbox>
<char>Apt</char><bbox>0.28, 0.601, 0.327, 0.651</bbox>
<char>42</char><bbox>0.377, 0.601, 0.416, 0.648</bbox>
<char>Shieung</char><bbox>0.445, 0.578, 0.53, 0.625</bbox>
<char>์ธ์ฒœ๋Œ€๊ณต์›</char><bbox>0.43, 0.621, 0.609, 0.684</bbox>
<char>๋ชจ๋ž˜๋‚ด์‹œ์žฅ์—ญ</char><bbox>0.651, 0.59, 0.873, 0.665</bbox>
<char>IncheonGrand</char><bbox>0.432, 0.681, 0.561, 0.723</bbox>
<char>Park</char><bbox>0.564, 0.681, 0.611, 0.723</bbox>
OCR Example

Citing the Model

If you use VARCO-VISION-14B in your research, please cite the following:

@misc{ju2024varcovisionexpandingfrontierskorean,
    title={VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models}, 
    author={Jeongho Ju and Daeyoung Kim and SunYoung Park and Youngjune Kim},
    year={2024},
    eprint={2411.19103},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2411.19103},