Table Classification Module Usage Tutorial¶

1. Overview¶

The Table Classification Module is a key component in computer vision systems, responsible for classifying input table images. The performance of this module directly affects the accuracy and efficiency of the entire table recognition process. The Table Classification Module typically receives table images as input and, using deep learning algorithms, classifies them into predefined categories based on the characteristics and content of the images, such as wired and wireless tables. The classification results from the Table Classification Module serve as output for use in table recognition pipelines.

2. Supported Model List¶

The inference time only includes the model inference time and does not include the time for pre- or post-processing. The "Regular Mode" values correspond to the local paddle_static inference engine.

Model	Model Download Link	Top1 Acc(%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)
PP-LCNet_x1_0_table_cls	Inference Model/Training Model	94.2	2.62 / 0.60	3.17 / 1.14	6.6

Test Environment Description:

Performance Test Environment
- Test Dataset: Internal evaluation dataset built by PaddleX.
- Hardware Configuration:
  - GPU: NVIDIA Tesla T4
  - CPU: Intel Xeon Gold 6271C @ 2.60GHz
- Software Environment:
  - Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6
  - paddlepaddle-gpu 3.0.0 / paddleocr 3.0.3
Inference Mode Explanation

Mode	GPU Configuration	CPU Configuration	Acceleration Technology Combination
Regular Mode	FP32 Precision / No TRT Acceleration	FP32 Precision / 8 Threads	PaddleInference
High-Performance Mode	Optimal combination of prior precision type and acceleration strategy	FP32 Precision / 8 Threads	Choose the optimal prior backend (Paddle/OpenVINO/TRT, etc.)

3. Quick Start¶

❗ Before starting quickly, please first install the PaddleOCR wheel package. For details, please refer to the installation tutorial.

You can quickly experience it with one command:

paddleocr table_classification -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg

The example above uses the paddle_static inference engine by default. To run it, first install PaddlePaddle by following PaddlePaddle Framework Installation.

If you choose transformers as the inference engine, make sure the Transformers environment is configured, and then run the following command:

# Use the transformers engine for inference
paddleocr table_classification -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg \
    --engine transformers

In most scenarios, the default paddle_static inference engine delivers better inference performance and is the recommended first choice.

Note: The official models would be download from HuggingFace by default. If can't access to HuggingFace, please set the environment variable PADDLE_PDX_MODEL_SOURCE="BOS" to change the model source to BOS. In the future, more model sources will be supported.

You can also integrate model inference from the table classification module into your project. Before running the following code, please download the sample image locally.

from paddleocr import TableClassification
model = TableClassification(model_name="PP-LCNet_x1_0_table_cls")
output = model.predict("table_recognition.jpg", batch_size=1)
for res in output:
    res.print(json_format=False)
    res.save_to_json("./output/res.json")

The example above uses the paddle_static inference engine by default. To run it, first install PaddlePaddle by following PaddlePaddle Framework Installation.

If you choose transformers as the inference engine, make sure the Transformers environment is configured, and then run the following code:

from paddleocr import TableClassification
model = TableClassification(
    model_name="PP-LCNet_x1_0_table_cls",
    engine="transformers",
)
output = model.predict("table_recognition.jpg", batch_size=1)
for res in output:
    res.print(json_format=False)
    res.save_to_json("./output/res.json")

In most scenarios, the default paddle_static inference engine delivers better inference performance and is the recommended first choice.

If you want to use the trained model with the paddle_dynamic or transformers engine, refer to the Weight Conversion section in the Inference Engine section below to convert the model from the pdparams format to the safetensors format using PaddleX.

After running, the result obtained is:

{'res': {'input_path': 'table_recognition.jpg', 'page_index': None, 'class_ids': array([0, 1], dtype=int32), 'scores': array([0.84421, 0.15579], dtype=float32), 'label_names': ['wired_table', 'wireless_table']}}

The parameter meanings are as follows:

input_path: Path of the input image
page_index: If the input is a PDF file, it indicates which page of the PDF it is; otherwise, it is None
class_ids: Class IDs of the prediction results
scores: Confidence scores of the prediction results
label_names: Class names of the prediction results

The visualized image is as follows:

The relevant methods, parameters, etc., are described as follows:

TableClassification instantiates the table classification model (taking PP-LCNet_x1_0_table_cls as an example here), with specific explanations as follows:

Parameter	Description	Type	Default
`model_name`	Meaning:Model name. Description: If set to `None`, `PP-LCNet_x1_0_table_cls` will be used.	`str\|None`	`None`
`model_dir`	Meaning:Model storage path.	`str\|None`	`None`
`device`	Meaning:Device for inference. Description: For example: `"cpu"`, `"gpu"`, `"npu"`, `"gpu:0"`, `"gpu:0,1"`. If multiple devices are specified, parallel inference will be performed. By default, GPU 0 is used if available; otherwise, CPU is used.	`str\|None`	`None`
`engine`	Meaning: Inference engine. Description: Supports `None` (the default), `paddle`, `paddle_static`, `paddle_dynamic`, and `transformers`. When left as `None`, local inference uses the `paddle_static` engine by default. For detailed descriptions, supported values, compatibility rules, and examples, see Inference Engine and Configuration.	`str\|None`	`None`
`engine_config`	Meaning: Inference-engine configuration. Description: Recommended together with `engine`. For supported fields, compatibility rules, and examples, see Inference Engine and Configuration.	`dict\|None`	`None`
`enable_hpi`	Meaning:Whether to enable high-performance inference.	`bool`	`False`
`use_tensorrt`	Meaning:Whether to use the Paddle Inference TensorRT subgraph engine. Description: If the model does not support acceleration through TensorRT, setting this flag will not enable acceleration. For Paddle with CUDA version 11.8, the compatible TensorRT version is 8.x (x>=6), and it is recommended to install TensorRT 8.6.1.6.	`bool`	`False`
`precision`	Meaning:Computation precision when using the Paddle Inference TensorRT subgraph engine. Description: Options: `"fp32"`, `"fp16"`.	`str`	`"fp32"`
`enable_mkldnn`	Meaning:Whether to enable MKL-DNN acceleration for inference. Description: If MKL-DNN is unavailable or the model does not support it, acceleration will not be used even if this flag is set.	`bool`	`True`
`mkldnn_cache_capacity`	Meaning:MKL-DNN cache capacity.	`int`	`10`
`cpu_threads`	Meaning:Number of threads to use for inference on CPUs.	`int`	`10`

Call the predict() method of the table classification model for inference prediction. This method will return a result list. Additionally, this module also provides a predict_iter() method. Both methods are consistent in terms of parameter acceptance and result return. The difference is that predict_iter() returns a generator, which can process and obtain prediction results step by step, suitable for handling large datasets or scenarios where memory saving is desired. You can choose to use either of these methods according to your actual needs. The predict() method has parameters input and batch_size, with specific explanations as follows:

Parameter	Description	Type	Default
`input`	Meaning:Input data to be predicted. Required. Description: Supports multiple input types: Python Var: e.g., `numpy.ndarray` representing image data str: Local image file or PDF file path: `/root/data/img.jpg`; URL: Image or PDF file network URL: Example; Directory: Should contain images for prediction, e.g., `/root/data/` (currently, PDF files in directories are not supported, PDF files need to be specified by file path) list: List elements should be of the above types, e.g., `[numpy.ndarray, numpy.ndarray]`, `["/root/data/img1.jpg", "/root/data/img2.jpg"]`, `["/root/data1", "/root/data2"]`	`Python Var\|str\|list`
`batch_size`	Meaning:Batch size. Description: Positive integer.	`int`	1

Process the prediction results. The prediction result for each sample is a corresponding Result object, which supports printing, saving as an image, and saving as a json file:

Method	Description	Parameter	Type	Parameter Description	Default Value
`print()`	Print result to terminal	`format_json`	`bool`	Whether to format the output content using `JSON` indentation	`True`
		`indent`	`int`	Specifies the indentation level to beautify the output `JSON` data, making it more readable, effective only when `format_json` is `True`	4
		`ensure_ascii`	`bool`	Controls whether to escape non-`ASCII` characters into `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` will retain the original characters, effective only when `format_json` is `True`	`False`
`save_to_json()`	Save the result as a json format file	`save_path`	`str`	The path to save the file. When specified as a directory, the saved file is named consistent with the input file type.	None
		`indent`	`int`	Specifies the indentation level to beautify the output `JSON` data, making it more readable, effective only when `format_json` is `True`	4
		`ensure_ascii`	`bool`	Controls whether to escape non-`ASCII` characters into `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` will retain the original characters, effective only when `format_json` is `True`	`False`

Additionally, the result can be obtained through attributes that provide the visualized images with results and the prediction results, as follows:

Attribute	Description
`json`	Get the prediction result in `json` format
`img`	Get the visualized image

4. Secondary Development¶

Since PaddleOCR does not directly provide training for the table classification module, if you need to train a table classification model, you can refer to the PaddleX Table Classification Module Secondary Development section for training. The trained model can be seamlessly integrated into the PaddleOCR API for inference.

If you want to use the paddle_dynamic or transformers engine with the trained model, please refer to the Weight Conversion section in Inference Engine later in this document to convert the model from the pdparams format to the safetensors format using PaddleX.

5. Inference Engine¶

For detailed descriptions, values, compatibility rules, and examples of the inference engine, please refer to Inference Engine and Configuration Description.

5.1 Speed Data¶

model	engine	Preprocessing (ms)	Inference (ms)	PostProcessing (ms)	End-to-End (ms)
PP-LCNet_x1_0_table_cls	paddle_static	3.56	3.38	0.06	7.11
	paddle_dynamic	3.57	7.77	0.07	11.52
	transformers	9.30	3.72	0.15	14.05

Test Environment Description:

Test Data: [Sample Image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg)
Hardware Configuration:
- GPU: NVIDIA A100 40G
- CPU: Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz
Software Environment:
- Ubuntu 22.04 / CUDA 12.6 / cuDNN 9.5
- paddlepaddle-gpu 3.2.1 / paddleocr 3.5 / transformers 5.4.0 / torch 2.10

5.2 Weight Conversion¶

When using the inference engine, the system will automatically download the official pre-trained model. If you need to use a self-trained model with the paddle_dynamic or transformers engine, please refer to the PaddleX Table Classification Module Weight Conversion section to convert the model from the pdparams format to the safetensors format using PaddleX. This allows seamless integration into the PaddleOCR API for inference.