PaddleClas/docs/en/tutorials/config_en.md

#Configuration

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Introduction

This document introduces the configuration(filed in config/*.yaml) of PaddleClas.

• Note: Some parameters do not appear in the yaml file (because they are not used for this file). During training or validation, you can use the command -o to update or add the specified parameters. For the example -o checkpoints=./ckp_path/ppcls, it means that the parameter checkpoints will be updated or added using the value ./ckp_path/ppcls.

Basic

name	detail	default value	optional value
mode	mode	"train"	["train"," valid"]
checkpoints	checkpoint model path for resuming training process	""	Str
last_epoch	last epoch for the training，used with checkpoints	-1	int
pretrained_model	pretrained model path	""	Str
load_static_weights	whether the pretrained model is saved in static mode	False	bool
model_save_dir	model stored path	""	Str
classes_num	class number	1000	int
total_images	total images	1281167	int
save_interval	save interval	1	int
validate	whether to validate when training	TRUE	bool
valid_interval	valid interval	1	int
epochs	epoch		int
topk	K value	5	int
image_shape	image size	[3，224，224]	list, shape: (3,)
use_mix	whether to use mixup	False	['True', 'False']
ls_epsilon	label_smoothing epsilon value	0	float
use_distillation	whether to use SSLD distillation training	False	bool

ARCHITECTURE

name	detail	default value	optional value
name	model name	"ResNet50_vd"	one of 23 architectures
params	model parameters	{}	extra dictionary for the model structure, parameters such as padding_type in EfficientNet can be set here

LEARNING_RATE

name	detail	default value	Optional value
function	decay type	"Linear"	["Linear", "Cosine", "Piecewise", "CosineWarmup"]
params.lr	initial learning rate	0.1	float
params.decay_epochs	milestone in piecewisedecay		list
params.gamma	gamma in piecewisedecay	0.1	float
params.warmup_epoch	warmup epoch	5	int
parmas.steps	decay steps in lineardecay	100	int
params.end_lr	end lr in lineardecay	0	float

OPTIMIZER

name	detail	default value	optional value
function	optimizer name	"Momentum"	["Momentum", "RmsProp"]
params.momentum	momentum value	0.9	float
regularizer.function	regularizer method name	"L2"	["L1", "L2"]
regularizer.factor	regularizer factor	0.0001	float

reader

name	detail
batch_size	batch size
num_workers	worker number
file_list	train list path
data_dir	train dataset path
shuffle_seed	seed

processing

function name	attribute name	detail
DecodeImage	to_rgb	decode to RGB
	to_np	to numpy
	channel_first	Channel first
RandCropImage	size	random crop
RandFlipImage		random flip
NormalizeImage	scale	normalize image
	mean	mean
	std	std
	order	order
ToCHWImage		to CHW
CropImage	size	crop size
ResizeImage	resize_short	resize according to short size

mix preprocessing

name	detail
MixupOperator.alpha	alpha value in mixup