--- comments: true --- # Table Cell Detection Module Usage Tutorial ## I. Overview The Table Cell Detection Module is a key component of the table recognition task, responsible for locating and marking each cell region in table images. The performance of this module directly affects the accuracy and efficiency of the entire table recognition process. The Table Cell Detection Module typically outputs bounding boxes for each cell region, which are then passed as input to the table recognition pipeline for further processing. ## II. Supported Model List
Model | Model Download Link | mAP(%) | GPU Inference Time (ms) [Regular Mode / High-Performance Mode] |
CPU Inference Time (ms) [Regular Mode / High-Performance Mode] |
Model Storage Size (MB) | Description |
---|---|---|---|---|---|---|
RT-DETR-L_wired_table_cell_det | Inference Model/Training Model | 82.7 | 33.47 / 27.02 | 402.55 / 256.56 | 124 | RT-DETR is a real-time end-to-end object detection model. The Baidu PaddlePaddle Vision team pre-trained on a self-built table cell detection dataset based on the RT-DETR-L as the base model, achieving good performance in detecting both wired and wireless table cells. |
RT-DETR-L_wireless_table_cell_det | Inference Model/Training Model |
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.) |
[xmin, ymin, xmax, ymax]
The visualized image is as follows:
Parameter | Description | Type | Default |
---|---|---|---|
model_name |
Model name. If set to None , RT-DETR-L_wired_table_cell_det will be used. |
str|None |
None |
model_dir |
Model storage path. | str|None |
None |
device |
Device for inference. 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 |
enable_hpi |
Whether to enable high-performance inference. | bool |
False |
use_tensorrt |
Whether to use the Paddle Inference TensorRT subgraph engine. 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. For Paddle with CUDA version 12.6, the compatible TensorRT version is 10.x (x>=5), and it is recommended to install TensorRT 10.5.0.18. |
bool |
False |
precision |
Computation precision when using the Paddle Inference TensorRT subgraph engine. Options: "fp32" , "fp16" . |
str |
"fp32" |
enable_mkldnn |
Whether to enable MKL-DNN acceleration for inference. 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 |
MKL-DNN cache capacity. | int |
10 |
cpu_threads |
Number of threads to use for inference on CPUs. | int |
10 |
img_size |
Input image size.
|
int|list|None |
None |
threshold |
Threshold for filtering out low-confidence prediction results.
|
float|dict|None |
None |
Parameter | Description | Type | Default |
---|---|---|---|
input |
Input data to be predicted. Required. Supports multiple input types:
|
Python Var|str|list |
|
batch_size |
Batch size, positive integer. | int |
1 |
threshold |
Same meaning as the instantiation parameters. If set to None , the instantiation value is used; otherwise, this parameter takes precedence. |
float|dict|None |
None |
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 |
||
save_to_img() |
Save the result as an image 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 |
Attribute | Description |
---|---|
json |
Get the prediction result in json format |
img |
Get the visualized image |