Upload 2 files
Browse files- .gitattributes +1 -0
- K-DTCBench.csv +3 -0
- K-DTCBench.md +95 -0
.gitattributes
CHANGED
|
@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 57 |
# Video files - compressed
|
| 58 |
*.mp4 filter=lfs diff=lfs merge=lfs -text
|
| 59 |
*.webm filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 57 |
# Video files - compressed
|
| 58 |
*.mp4 filter=lfs diff=lfs merge=lfs -text
|
| 59 |
*.webm filter=lfs diff=lfs merge=lfs -text
|
| 60 |
+
K-DTCBench.csv filter=lfs diff=lfs merge=lfs -text
|
K-DTCBench.csv
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3ee9d974c425105a308d0bbdaa3ee05fe82db372ef69e9185b249deec6f48da1
|
| 3 |
+
size 12886645
|
K-DTCBench.md
ADDED
|
@@ -0,0 +1,95 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-nc-4.0
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
# K-DTCBench
|
| 6 |
+
|
| 7 |
+
We introduce **K-DTCBench**, a newly developed Korean benchmark featuring both computer-generated and handwritten documents, tables, and charts.
|
| 8 |
+
It consists of 80 questions for each image type and two questions per image, summing up to 240 questions in total.
|
| 9 |
+
This benchmark is designed to evaluate whether vision-language models can process images in different formats and be applicable for diverse domains.
|
| 10 |
+
All images are generated with made-up values and statements for evaluation purposes only. We scanned hand-written documents/tables/charts, or created digital objects with matplotlib library to build K-DTCBench.
|
| 11 |
+
The proportions of digital and hand-written images are equal, each constituting 50%.
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
For more details, Please refer to the [VARCO-VISION technical report(Coming Soon)]().
|
| 15 |
+
|
| 16 |
+
<table>
|
| 17 |
+
<tr>
|
| 18 |
+
<th>Category</th>
|
| 19 |
+
<th>Image</th>
|
| 20 |
+
<th>K-DTCBench</th>
|
| 21 |
+
</tr>
|
| 22 |
+
<tr>
|
| 23 |
+
<td align="center">document</td>
|
| 24 |
+
<td width=350><img src="https://cdn-uploads.huggingface.co/production/uploads/624ceaa38746b2f5773c2d1c/Ipi4HR73P-PDC5XcgP3WF.png"></td>
|
| 25 |
+
<td>
|
| 26 |
+
<strong>question:</strong> 보고서의 주요 내용이 아닌 것은 무엇인가요?
|
| 27 |
+
<br>
|
| 28 |
+
<strong>A:</strong> 안전 인프라 확충
|
| 29 |
+
<br>
|
| 30 |
+
<strong>B:</strong> 재난 및 사고 예방 체계 구축
|
| 31 |
+
<br>
|
| 32 |
+
<strong>C:</strong> 시민 안전 교육 강화
|
| 33 |
+
<br>
|
| 34 |
+
<strong>D:</strong> 긴급 대응 시스템 개선
|
| 35 |
+
</td>
|
| 36 |
+
</tr>
|
| 37 |
+
<tr>
|
| 38 |
+
<td align="center">table</td>
|
| 39 |
+
<td width=350><img src="https://cdn-uploads.huggingface.co/production/uploads/624ceaa38746b2f5773c2d1c/dz_FuPnpZ5P4P3LEB5PZ0.png"></td>
|
| 40 |
+
<td>
|
| 41 |
+
<strong>question:</strong> 인프라 구축 항목의 점수는 몇 점인가요?
|
| 42 |
+
<br>
|
| 43 |
+
<strong>A:</strong> 4
|
| 44 |
+
<br>
|
| 45 |
+
<strong>B:</strong> 6
|
| 46 |
+
<br>
|
| 47 |
+
<strong>C:</strong> 8
|
| 48 |
+
<br>
|
| 49 |
+
<strong>D:</strong> 10
|
| 50 |
+
</td>
|
| 51 |
+
</tr>
|
| 52 |
+
<tr>
|
| 53 |
+
<td align="center">chart</td>
|
| 54 |
+
<td width=350><img src="https://cdn-uploads.huggingface.co/production/uploads/624ceaa38746b2f5773c2d1c/IbNMPPgd974SbCAsz6zIS.png"></td>
|
| 55 |
+
<td>
|
| 56 |
+
<strong>question:</strong> 직장인들이 퇴근 후 두 번째로 선호하는 활동은 무엇인가요?
|
| 57 |
+
<br>
|
| 58 |
+
<strong>A:</strong> 운동
|
| 59 |
+
<br>
|
| 60 |
+
<strong>B:</strong> 여가활동
|
| 61 |
+
<br>
|
| 62 |
+
<strong>C:</strong> 자기개발
|
| 63 |
+
<br>
|
| 64 |
+
<strong>D:</strong> 휴식
|
| 65 |
+
</td>
|
| 66 |
+
</tr>
|
| 67 |
+
</table>
|
| 68 |
+
|
| 69 |
+
<br>
|
| 70 |
+
|
| 71 |
+
## Inference Prompt
|
| 72 |
+
```
|
| 73 |
+
<image>
|
| 74 |
+
{question}
|
| 75 |
+
Options: A: {A}, B: {B}, C: {C}, D: {D}
|
| 76 |
+
|
| 77 |
+
주어진 선택지 중 해당 옵션의 문자로 직접 답하세요.
|
| 78 |
+
```
|
| 79 |
+
|
| 80 |
+
<br>
|
| 81 |
+
|
| 82 |
+
## Results
|
| 83 |
+
Below are the evaluation results of various vision-language models, including [VARCO-VISION-14B]() on K-DTCBench.
|
| 84 |
+
|
| 85 |
+
| | VARCO-VISION-14B | Pangea-7B | Pixtral-12B | Molmo-7B-D | Qwen2-VL-7B-Instruct | LLaVA-One-Vision-7B |
|
| 86 |
+
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|
| 87 |
+
| K-DTCBench | **84.58** | 48.33 | 27.50 | 45.83 | 75.00 | 52.91 |
|
| 88 |
+
|
| 89 |
+
<br>
|
| 90 |
+
|
| 91 |
+
## Citation
|
| 92 |
+
(bibtex will be updated soon..) If you use K-DTCBench in your research, please cite the following:
|
| 93 |
+
```
|
| 94 |
+
|
| 95 |
+
```
|