Could anyone explain YML parameter to me or where can I find the explain page?

[felixho789]

Could anyone explain YML parameter to me or where can I find the explain page?

Following is an yml code:
Global:
debug: false
use_gpu: true
epoch_num: &epoch_num 1000
log_smooth_window: 20
print_batch_step: 1
save_model_dir: ./output/DET
save_epoch_step: 500
eval_batch_step:

• 0
• 3000
  cal_metric_during_train: false
  checkpoints:
  pretrained_model: /MODEL/DET/ch_PP-OCRv4_det_server_train/best_accuracy.pdparams
  save_inference_dir: null
  use_visualdl: false
  infer_img: C:\Program Files\Python310\Lib\site-packages\paddleocr\DATASET
  save_res_path: ./checkpoints/det_db/predicts_db.txt
  d2s_train_image_shape: [3, 96, 320]
  distributed: true

Architecture:
model_type: det
algorithm: DB
Transform: null
Backbone:
name: PPHGNet_small
det: True
Neck:
name: LKPAN
out_channels: 256
intracl: true
Head:
name: PFHeadLocal
k: 50
mode: "large"
fix_nan: True

Loss:
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3

Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001 #(8*8c)
warmup_epoch: 2
regularizer:
name: L2
factor: 1e-6

PostProcess:
name: DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5

Metric:
name: DetMetric
main_indicator: hmean

Train:
dataset:
name: SimpleDataSet
data_dir: C:\Program Files\Python310\Lib\site-packages\paddleocr\DATASET
label_file_list:
- C:\Program Files\Python310\Lib\site-packages\paddleocr\DATASET\Label.txt
ratio_list: [1.0]
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- DetLabelEncode: null
- CopyPaste: null
- IaaAugment:
augmenter_args:
- type: Fliplr
args:
p: 0.5
- type: Affine
args:
rotate:
- -10
- 10
- type: Resize
args:
size:
- 0.5
- 3
- EastRandomCropData:
size:
- 74
- 246
max_tries: 50
keep_ratio: true
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
total_epoch: *epoch_num
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
total_epoch: *epoch_num
- NormalizeImage:
scale: 1./255.
mean:
- 0.485
- 0.456
- 0.406
std:
- 0.229
- 0.224
- 0.225
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys:
- image
- threshold_map
- threshold_mask
- shrink_map
- shrink_mask
loader:
shuffle: true
drop_last: false
batch_size_per_card: 12
num_workers: 8

Eval:
dataset:
name: SimpleDataSet
data_dir: C:\Program Files\Python310\Lib\site-packages\paddleocr\DATASET
label_file_list:
- C:\Program Files\Python310\Lib\site-packages\paddleocr\DATASET\Label.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- DetLabelEncode: null
- DetResizeForTest:
- NormalizeImage:
scale: 1./255.
mean:
- 0.485
- 0.456
- 0.406
std:
- 0.229
- 0.224
- 0.225
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys:
- image
- shape
- polys
- ignore_tags
loader:
shuffle: false
drop_last: false
batch_size_per_card: 1
num_workers: 2
profiler_options: null

[GreatV]

YAML (YML) is a human-readable data serialization format commonly used for configuration files. In the provided YML code, parameters are structured hierarchically to define various aspects of a machine learning model training process. Here’s a breakdown of key elements:

Key Components of the YML File:

1. Global Settings (Global):

   • Defines overall configurations, such as enabling debugging (debug: false), using GPU (use_gpu: true), total training epochs (epoch_num: 1000), and paths for saving models and results.
   • epoch_num: &epoch_num 1000 creates an anchor (&epoch_num) that can be referenced elsewhere using *epoch_num.

2. Model Architecture (Architecture):

   • Specifies the type of model (model_type: det for detection).
   • Defines the algorithm (algorithm: DB for Differentiable Binarization).
   • Includes details of the backbone (Backbone), neck (Neck), and head (Head) components.

3. Loss Function (Loss):

   • Specifies the loss function (name: DBLoss).
   • Defines loss parameters like balance_loss, main_loss_type: DiceLoss, and coefficients (alpha: 5, beta: 10).

4. Optimizer (Optimizer):

   • Uses the Adam optimizer (name: Adam).
   • Defines learning rate schedule (Cosine decay with learning_rate: 0.001).
   • Includes L2 regularization (factor: 1e-6).

5. Post-processing (PostProcess):

   • Specifies post-processing steps like thresholding (thresh: 0.3, box_thresh: 0.6), candidate selection (max_candidates: 1000), and unclip ratio (unclip_ratio: 1.5).

6. Evaluation Metrics (Metric):

   • Defines the metric for evaluation (name: DetMetric, main_indicator: hmean).

7. Training Configuration (Train):

   • Defines dataset location (data_dir), label file, and data augmentation (transforms).
   • Specifies data preprocessing steps like DecodeImage, NormalizeImage, and MakeBorderMap.
   • Uses the referenced epoch number (total_epoch: *epoch_num).

8. Evaluation Configuration (Eval):

   • Similar to Train, but for validation data.
   • Includes transformations like DetResizeForTest and NormalizeImage.

Where to Find More Information?

• If you are looking for an explanation of YAML syntax, refer to YAML Documentation.
• For specific details on how these parameters affect model training, check the documentation of the framework you are using (e.g., PaddleOCR).

Let me know if you need further clarification! 😊

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