---
library_name: transformers
license: apache-2.0
pipeline_tag: image-segmentation
---

# DIT-base-layout-detection

We present the model cmarkea/dit-base-layout-detection, which allows extracting different layouts (Text, Picture, Caption, Footnote, etc.) from an image of a document.
This is a fine-tuning of the model [dit-base](https://huggingface.co/microsoft/dit-base) on the [DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet)
dataset. This model can jointly predict masks and bounding boxes for documentary objects. It is ideal for processing documentary corpora to be ingested into an
ODQA system.

This model allows extracting 11 entities, which are: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, and Title.

## Performance

In this section, we will assess the model's performance by separately considering semantic segmentation and object detection. In both cases, no post-processing was
applied after estimation.

For semantic segmentation, we will use the F1-score to evaluate the classification of each pixel. For object detection, we will assess performance based on the
Generalized Intersection over Union (GIoU) and the accuracy of the predicted bounding box class. The evaluation is conducted on 500 pages from the PDF evaluation
dataset of DocLayNet.

|      Class     | f1-score (x100) | GIoU (x100) | accuracy (x100) |
|:--------------:|:---------------:|:-----------:|:---------------:|
|   Background   |      94.98      |      NA     |        NA       |
|     Caption    |      75.54      |    55.61    |      72.62      |
|    Footnote    |      72.29      |    50.08    |      70.97      |
|     Formula    |      82.29      |    49.91    |      94.48      |
|    List-item   |      67.56      |    35.19    |      69         |
|   Page-footer  |      83.93      |    57.99    |      94.06      |
|   Page-header  |      62.33      |    65.25    |      79.39      |
|     Picture    |      78.32      |    58.22    |      92.71      |
| Section-header |      69.55      |    56.64    |      78.29      |
|      Table     |      83.69      |    63.03    |      90.13      |
|      Text      |      90.94      |    51.89    |      88.09      |
|      Title     |      61.19      |    52.64    |      70         |

## Benchmark

Now, let's compare the performance of this model with other models.

|      Class                                                                                    | f1-score (x100) | GIoU (x100) | accuracy (x100) |
|:---------------------------------------------------------------------------------------------:|:---------------:|:-----------:|:---------------:|
| cmarkea/dit-base-layout-detection                                                             |      90.77      |    56.29    |      85.26      |
| [cmarkea/detr-layout-detection](https://huggingface.co/cmarkea/detr-layout-detection)         |      84.23      |    43.84    |      71.98      |

### Direct Use

```python
import torch
from transformers import AutoImageProcessor, AutoModel

img_proc = AutoImageProcessor.from_pretrained(
    "ArkeaIAF/dit-base-layout-detection"
)
model = AutoModel.from_pretrained(
    "ArkeaIAF/dit-base-layout-detection"
)

with torch.inference_mode():
    input_ids = img_proc(img, return_tensors='pt')
    segmentation = model(**input_ids)

segmentation_mask = img_proc.post_process_semantic_segmentation(
    segmentation,
    target_sizes=[img.size[::-1]]
)
```

### Citation

```
@online{DeDitLay,
  AUTHOR = {Cyrile Delestre},
  URL = {https://huggingface.co/cmarkea/dit-base-layout-detection},
  YEAR = {2024},
  KEYWORDS = {Image Processing ; Transformers ; Layout},
}
```