| Parameter |
Description |
Type |
Options |
Default Value |
model_name |
Name of the model |
str |
None |
None |
model_dir |
Path to store the model |
str |
None |
None |
device |
The device used for model inference |
str |
It supports specifying specific GPU card numbers, such as "gpu:0", other hardware card numbers, such as "npu:0", or CPU, such as "cpu". |
gpu:0 |
use_hpip |
Whether to enable high-performance inference. |
bool |
None |
False |
img_size |
Size of the input image; if not specified, the default configuration of the PaddleX official model will be used |
int/list |
- int, e.g., 640, means resizing the input image to 640x640
- List, e.g., [640, 512], means resizing the input image to a width of 640 and a height of 512
|
None |
threshold |
Threshold to filter out low-confidence predictions; if not specified, the default configuration of the PaddleX official model will be used. In table cell detection tasks, lowering the threshold appropriately may help to obtain more accurate results. |
float/dict |
- float, e.g., 0.2, means filtering out all bounding boxes with a confidence score less than 0.2
- Dictionary, with keys as int representing
cls_id and values as float thresholds. For example, {0: 0.45, 2: 0.48, 7: 0.4} means applying a threshold of 0.45 for class ID 0, 0.48 for class ID 2, and 0.4 for class ID 7
|
None |
* The `model_name` must be specified. Once `model_name` is specified, the default model parameters from PaddleX will be used. If `model_dir` is specified, the user-defined model will be used instead.
* The `predict()` method of the table cell detection model is called to perform inference and prediction. The parameters of the `predict()` method include `input`, `batch_size`, and `threshold`, with the following details:
| Parameter |
Parameter Description |
Parameter Type |
Optional |
Default Value |
input |
Data to be predicted, supporting multiple input types |
Python Var/str/list |
- Python variable, such as
numpy.ndarray representing image data
- File path, such as the local path of an image file:
/root/data/img.jpg
- URL link, such as the network URL of an image file: Example
- Local directory, the directory must contain files to be predicted, such as the local path:
/root/data/
- List, the elements of the list should be of the above-mentioned data types, such as
[numpy.ndarray, numpy.ndarray], [\"/root/data/img1.jpg\", \"/root/data/img2.jpg\"], [\"/root/data1\", \"/root/data2\"]
|
None |
batch_size |
Batch size |
int |
Any integer |
1 |
threshold |
Threshold for filtering out low-confidence prediction results; if not specified, the threshold parameter specified in creat_model will be used by default, and if creat_model also does not specify it, the default PaddleX official model configuration will be used |
float/dict |
- float, such as 0.2, indicating that all target boxes with a threshold less than 0.2 will be filtered out
- Dictionary, the key of the dictionary is of type int, representing
cls_id, and the val is a float type threshold. For example, {0: 0.45, 2: 0.48, 7: 0.4}, indicating that a threshold of 0.45 is applied to the class with cls_id 0, a threshold of 0.48 is applied to the class with cls_id 1, and a threshold of 0.4 is applied to the class with cls_id 7
|
None |
* Process the prediction results, where the prediction result of each sample is a corresponding Result object, and supports operations such as printing, saving as an image, and saving as a `json` file:
| Method |
Method Description |
Parameter |
Parameter Type |
Parameter Description |
Default Value |
print() |
Print the result to the terminal |
format_json |
bool |
Whether to format the output content using JSON indentation |
True |
indent |
int |
Specify the indentation level to beautify the output JSON data, making it more readable, only effective when format_json is True |
4 |
ensure_ascii |
bool |
Control whether to escape non-ASCII characters to Unicode. When set to True, all non-ASCII characters will be escaped; False retains the original characters, only effective when format_json is True |
False |
save_to_json() |
Save the result as a JSON file |
save_path |
str |
The file path to save to, and when it is a directory, the saved file name is consistent with the input file name |
None |
indent |
int |
Specify the indentation level to beautify the output JSON data, making it more readable, only effective when format_json is True |
4 |
ensure_ascii |
bool |
Control whether to escape non-ASCII characters to Unicode. When set to True, all non-ASCII characters will be escaped; False retains the original characters, only effective when format_json is True |
False |
save_to_img() |
Save the result as an image file |
save_path |
str |
The file path to save to, and when it is a directory, the saved file name is consistent with the input file name |
None |
* Additionally, it also supports obtaining visualized images with results and prediction results through attributes, as follows:
👉 Verification Result Details (Click to Expand)
The specific content of the verification result file is as follows:
```json
"done_flag": true,
"check_pass": true,
"attributes": {
"num_classes": 1,
"train_samples": 230,
"train_sample_paths": [
"check_dataset\/demo_img\/img_45_2.png",
"check_dataset\/demo_img\/img_69_1.png",
"check_dataset\/demo_img\/img_99_1.png",
"check_dataset\/demo_img\/img_6_1.png",
"check_dataset\/demo_img\/img_47_3.png",
"check_dataset\/demo_img\/img_54_2.png",
"check_dataset\/demo_img\/img_25_1.png",
"check_dataset\/demo_img\/img_73_1.png",
"check_dataset\/demo_img\/img_51_2.png",
"check_dataset\/demo_img\/img_93_3.png"
],
"val_samples": 26,
"val_sample_paths": [
"check_dataset\/demo_img\/img_88_2.png",
"check_dataset\/demo_img\/img_156_0.png",
"check_dataset\/demo_img\/img_43_4.png",
"check_dataset\/demo_img\/img_2_4.png",
"check_dataset\/demo_img\/img_42_4.png",
"check_dataset\/demo_img\/img_49_0.png",
"check_dataset\/demo_img\/img_45_1.png",
"check_dataset\/demo_img\/img_140_0.png",
"check_dataset\/demo_img\/img_5_1.png",
"check_dataset\/demo_img\/img_26_3.png"
]
},
"analysis": {
"histogram": "check_dataset\/histogram.png"
},
"dataset_path": "cells_det_coco_examples",
"show_type": "image",
"dataset_type": "COCODetDataset"
```
In the above verification results, check_pass being true indicates that the dataset format meets the requirements. The explanations for other metrics are as follows:
attributes.num_classes: The number of classes in this dataset is 1;attributes.train_samples: The number of training samples in this dataset is 230;attributes.val_samples: The number of validation samples in this dataset is 26;attributes.train_sample_paths: A list of relative paths to the visualization images of the training samples in this dataset;attributes.val_sample_paths: A list of relative paths to the visualization images of the validation samples in this dataset;
In addition, the dataset verification has analyzed the distribution of sample counts for all classes in the dataset and plotted a histogram (histogram.png):
👉 Details of Format Conversion/Dataset Splitting (Click to Expand)
(1) Dataset Format Conversion
Table cell detection supports converting datasets in VOC and LabelMe formats to the COCO format.
Parameters related to dataset verification can be set by modifying the fields under CheckDataset in the configuration file. Some example explanations of parameters in the configuration file are as follows:
CheckDataset:convert:enable: Whether to perform dataset format conversion. Table cell detection supports converting datasets in VOC and LabelMe formats to the COCO format. The default is False;src_dataset_type: If performing dataset format conversion, the source dataset format must be set. The default is null, and the available options are VOC, LabelMe, VOCWithUnlabeled, and LabelMeWithUnlabeled. For example, if you want to convert a dataset in LabelMe format to COCO format, using the following LabelMe dataset as an example, you need to modify the configuration as follows:
cd /path/to/paddlex
wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/det_labelme_examples.tar -P ./dataset
tar -xf ./dataset/det_labelme_examples.tar -C ./dataset/
......
CheckDataset:
......
convert:
enable: True
src_dataset_type: LabelMe
......
Then execute the command:
python main.py -c paddlex/configs/modules/table_cells_detection/RT-DETR-L_wired_table_cell_det.yaml \
-o Global.mode=check_dataset \
-o Global.dataset_dir=./dataset/det_labelme_examples
Of course, the above parameters can also be set by adding command-line arguments. For example, for a dataset in LabelMe format:
python main.py -c paddlex/configs/modules/table_cells_detection/RT-DETR-L_wired_table_cell_det.yaml \
-o Global.mode=check_dataset \
-o Global.dataset_dir=./dataset/det_labelme_examples \
-o CheckDataset.convert.enable=True \
-o CheckDataset.convert.src_dataset_type=LabelMe
(2) Dataset Splitting
Parameters for dataset splitting can be set by modifying the fields under CheckDataset in the configuration file. Some example explanations of parameters in the configuration file are as follows:
CheckDataset:split:enable: Whether to re-split the dataset. It is set to True when performing dataset format conversion. The default is False;train_percent: If re-splitting the dataset, you need to set the percentage of the training set. It is an integer between 0 and 100, and it must sum up to 100 with the value of val_percent;val_percent: If re-splitting the dataset, you need to set the percentage of the validation set. It is an integer between 0 and 100, and it must sum up to 100 with the value of train_percent. For example, if you want to re-split the dataset with 90% for training and 10% for validation, you need to modify the configuration file as follows:
......
CheckDataset:
......
split:
enable: True
train_percent: 90
val_percent: 10
......
Then execute the command:
python main.py -c paddlex/configs/modules/table_cells_detection/RT-DETR-L_wired_table_cell_det.yaml \
-o Global.mode=check_dataset \
-o Global.dataset_dir=./dataset/cells_det_coco_examples
After the dataset splitting is executed, the original annotation files will be renamed to xxx.bak in the original path.
The above parameters can also be set by adding command-line arguments:
python main.py -c paddlex/configs/modules/table_cells_detection/RT-DETR-L_wired_table_cell_det.yaml \
-o Global.mode=check_dataset \
-o Global.dataset_dir=./dataset/cells_det_coco_examples \
-o CheckDataset.split.enable=True \
-o CheckDataset.split.train_percent=90 \
-o CheckDataset.split.val_percent=10
👉 More Information (Click to Expand)
When evaluating the model, the path to the model weights file needs to be specified. Each configuration file has a default weight save path built-in. If you need to change it, you can simply set it by appending a command-line parameter, such as -o Evaluate.weight_path=./output/best_model/best_model.pdparams.
After the model evaluation is completed, an evaluate_result.json file will be generated, which records the evaluation results. Specifically, it records whether the evaluation task was completed normally and the model's evaluation metrics, including AP.
