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docling-project/DocumentFigureClassifier-v2.5

sourceHugging Facemitupdated 6mo agoView on Hugging Face

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EfficientNet-B0 Document Figure Classifier v2.5

This is an image classification model based on Google EfficientNet-B0, fine-tuned on a subset of the subset of HuggingFace/finepdfs to classify document figures into one of the following 26 categories:

1.logo
2.photograph
3.icon
4.engineering_drawing
5.line_chart
6.bar_chart
7.other
8.table
9.flow_chart
10.screenshot_from_computer
11.signature
12.screenshot_from_manual
13.geographical_map
14.pie_chart
15.page_thumbnail
16.stamp
17.music
18.calendar
19.qr_code
20.bar_code
21.full_page_image
22.scatter_plot
23.chemistry_structure
24.topographical_map
25.crossword_puzzle
26.box_plot

Model Performance

Note: This model uses the same architecture and implementation as v2.0. The improved performance is achieved by training on a dataset that is 10 times larger than the one used for v2.0.

The model was evaluated on a held-out test set from the finepdfs dataset with the following metrics:

Metric	v2.5	v2.0	Improvement
Accuracy	0.90703	0.87053	+3.65%
Balanced Accuracy	0.68836	0.60231	+8.61%
Macro F1	0.68942	0.60144	+8.80%
Weighted F1	0.90716	0.87270	+3.45%
Cohen's Kappa	0.87449	0.82563	+4.89%

Per-Label Performance

Label	Precision (v2.5)	Recall (v2.5)	Precision (v2.0)	Recall (v2.0)
logo	0.92807	0.91816	0.88317	0.88728
photograph	0.90966	0.96029	0.88169	0.93359
icon	0.83605	0.82678	0.79281	0.72133
engineering_drawing	0.71689	0.81172	0.58795	0.71555
line_chart	0.73055	0.92117	0.75865	0.84576
bar_chart	0.88599	0.92720	0.72624	0.93883
other	0.41893	0.38213	0.28239	0.37312
table	0.98636	0.96765	0.97950	0.95250
flow_chart	0.75926	0.82425	0.61527	0.81518
screenshot_from_computer	0.85952	0.71980	0.80510	0.65844
signature	0.89020	0.85971	0.91852	0.80914
screenshot_from_manual	0.48559	0.34543	0.34748	0.20662
geographical_map	0.86780	0.85219	0.82959	0.80720
pie_chart	0.96880	0.94220	0.89903	0.93931
page_thumbnail	0.52008	0.35188	0.40194	0.21475
stamp	0.71269	0.41794	0.63492	0.26258
music	0.48037	0.57778	0.76955	0.51944
calendar	0.52880	0.28775	0.51176	0.24786
qr_code	0.95694	0.93240	0.97500	0.90909
bar_code	0.34244	0.84305	0.12087	0.82063
full_page_image	0.40323	0.65789	0.43750	0.28116
scatter_plot	0.66848	0.67213	0.60386	0.68306
chemistry_structure	0.72781	0.65426	0.77444	0.54787
topographical_map	0.83333	0.38462	0.68750	0.28205
crossword_puzzle	0.57143	0.21622	0.80000	0.21622
box_plot	0.85714	0.64286	1.00000	0.07143

How to use - Transformers

Example of how to classify an image into one of the 26 classes using transformers:

python

import torch
import torchvision.transforms as transforms

from transformers import EfficientNetForImageClassification
from PIL import Image
import requests


urls = [
    'http://images.cocodataset.org/val2017/000000039769.jpg',
    'http://images.cocodataset.org/test-stuff2017/000000001750.jpg',
    'http://images.cocodataset.org/test-stuff2017/000000000001.jpg'
]

image_processor = transforms.Compose(
    [
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.47853944, 0.4732864, 0.47434163],
        ),
    ]
)

images = []
for url in urls:
    image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
    image = image_processor(image)
    images.append(image)


model_id = 'docling-project/DocumentFigureClassifier-v2.5'

model = EfficientNetForImageClassification.from_pretrained(model_id)

labels = model.config.id2label

device = torch.device("cpu")

torch_images = torch.stack(images).to(device)

with torch.no_grad():
    logits = model(torch_images).logits  # (batch_size, num_classes)
    probs_batch = logits.softmax(dim=1)  # (batch_size, num_classes)
    probs_batch = probs_batch.cpu().numpy().tolist()

for idx, probs_image in enumerate(probs_batch):
    preds = [(labels[i], prob) for i, prob in enumerate(probs_image)]
    preds.sort(key=lambda t: t[1], reverse=True)
    print(f"{idx}: {preds}")

How to use - ONNX

Example of how to classify an image into one of the 26 classes using onnx runtime:

python

import onnxruntime

import numpy as np
import torchvision.transforms as transforms

from PIL import Image
import requests

LABELS = [
    "logo",
    "photograph",
    "icon",
    "engineering_drawing",
    "line_chart",
    "bar_chart",
    "other",
    "table",
    "flow_chart",
    "screenshot_from_computer",
    "signature",
    "screenshot_from_manual",
    "geographical_map",
    "pie_chart",
    "page_thumbnail",
    "stamp",
    "music",
    "calendar",
    "qr_code",
    "bar_code",
    "full_page_image",
    "scatter_plot",
    "chemistry_structure",
    "topographical_map",
    "crossword_puzzle",
    "box_plot"
]


urls = [
    'http://images.cocodataset.org/val2017/000000039769.jpg',
    'http://images.cocodataset.org/test-stuff2017/000000001750.jpg',
    'http://images.cocodataset.org/test-stuff2017/000000000001.jpg'
]

images = []
for url in urls:
    image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
    images.append(image)


image_processor = transforms.Compose(
    [
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.47853944, 0.4732864, 0.47434163],
        ),
    ]
)


processed_images_onnx = [image_processor(image).unsqueeze(0) for image in images]

# onnx needs numpy as input
onnx_inputs = [item.numpy(force=True) for item in processed_images_onnx]

# pack into a batch
onnx_inputs = np.concatenate(onnx_inputs, axis=0)

ort_session = onnxruntime.InferenceSession(
    "./DocumentFigureClassifier-v2_5-onnx/model.onnx",
    providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
)


for item in ort_session.run(None, {'input': onnx_inputs}):
    for x in iter(item):
        pred = x.argmax()
        print(LABELS[pred])

Training Data

This model was trained on a subset of the subset of HuggingFace/finepdfs, a large-scale dataset for document understanding tasks.

Citation

If you use this model in your work, please cite the following papers:

@article{Tan2019EfficientNetRM,
  title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
  author={Mingxing Tan and Quoc V. Le},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.11946}
}

@techreport{Docling,
  author = {Deep Search Team},
  month = {8},
  title = {{Docling Technical Report}},
  url={https://arxiv.org/abs/2408.09869},
  eprint={2408.09869},
  doi = "10.48550/arXiv.2408.09869",
  version = {1.0.0},
  year = {2024}
}