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cmarkea/detr-layout-detection

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Model Card

DETR-layout-detection

We present the model cmarkea/detr-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 detr-resnet-50 on the 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	95.82	NA	NA
Caption	82.68	74.71	69.05
Footnote	78.19	74.71	74.19
Formula	87.25	76.31	97.79
List-item	81.43	77.0	90.62
Page-footer	82.01	69.86	96.64
Page-header	68.32	77.68	88.3
Picture	81.04	81.84	90.88
Section-header	73.52	73.46	85.96
Table	78.59	85.45	90.58
Text	91.93	83.16	91.8
Title	70.38	74.13	63.33

Benchmark

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

Model	f1-score (x100)	GIoU (x100)	accuracy (x100)
cmarkea/detr-layout-detection	91.27	80.66	90.46
cmarkea/dit-base-layout-detection	90.77	56.29	85.26

Direct Use

python

from transformers import AutoImageProcessor
from transformers.models.detr import DetrForSegmentation

img_proc = AutoImageProcessor.from_pretrained(
    "cmarkea/detr-layout-detection"
)
model = DetrForSegmentation.from_pretrained(
    "cmarkea/detr-layout-detection"
)

img: PIL.Image

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

threshold=0.4

segmentation_mask = img_proc.post_process_segmentation(
    output,
    threshold=threshold,
    target_sizes=[img.size[::-1]]
)

bbox_pred = img_proc.post_process_object_detection(
    output,
    threshold=threshold,
    target_sizes=[img.size[::-1]]
)

Example

example

Citation

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