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config_description_en.md

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Configuration Instruction

Introdction

The parameters in the PaddleClas configuration file(ppcls/configs/*.yaml)are described for you to customize or modify the hyperparameter configuration more quickly.

Details

1. Classification model

Here the configuration of ResNet50_vd onImageNet-1kis used as an example to explain the each parameter in detail. Configure Path.

1.1Global Configuration

Parameter name Specific meaning Defult value Optional value
checkpoints Breakpoint model path for resuming training null str
pretrained_model Pre-trained model path null str
output_dir Save model path "./output/" str
save_interval How many epochs to save the model at each interval 1 int
eval_during_train Whether to evaluate at training True bool
eval_interval How many epochs to evaluate at each interval 1 int
epochs Total number of epochs in training int
print_batch_step How many mini-batches to print out at each interval 10 int
use_visualdl Whether to visualize the training process with visualdl False bool
image_shape Image size [3，224，224] list, shape: (3,)
save_inference_dir Inference model save path "./inference" str
eval_mode Model of eval "classification" "retrieval"

Note：The http address of pre-trained model can be filled in the pretrained_model

1.2 Architecture

Parameter name Specific meaning Defult value Optional value
name Model Arch name ResNet50 PaddleClas model arch
class_num Category number 1000 int
pretrained Pre-trained model False bool， str

Note: Here pretrained can be set to True or False, so does the path of the weights. In addition, the pretrained is disabled when Global.pretrained_model is also set to the corresponding path.

1.3 Loss function

Parameter name Specific meaning Defult value Optional value
CELoss cross-entropy loss function —— ——
CELoss.weight The weight of CELoss in the whole Loss 1.0 float
CELoss.epsilon The epsilon value of label_smooth in CELoss 0.1 float，between 0 and 1

1.4 Optimizer

Parameter name Specific meaning Defult value Optional value
name optimizer method name "Momentum" Other optimizer including "RmsProp"
momentum momentum value 0.9 float
lr.name method of dropping learning rate "Cosine" Other dropping methods of "Linear" and "Piecewise"
lr.learning_rate initial value of learning rate 0.1 float
lr.warmup_epoch warmup rounds 0 int，such as 5
regularizer.name regularization method name "L2" ["L1", "L2"]
regularizer.coeff regularization factor 0.00007 float

Note：The new parameters may be different when lr.name is different , as when lr.name=Piecewise, the following parameters need to be added:

  lr:
    name: Piecewise
    learning_rate: 0.1
    decay_epochs: [30, 60, 90]
    values: [0.1, 0.01, 0.001, 0.0001]

Referring to learning_rate.py for adding method and parameters.

1.5 Data reading module（DataLoader）

1.5.1 dataset
Parameter name Specific meaning Defult value Optional value
name The name of the class to read the data ImageNetDataset VeriWild and other Dataet type
image_root The path where the dataset is stored ./dataset/ILSVRC2012/ str
cls_label_path data label list ./dataset/ILSVRC2012/train_list.txt str
transform_ops data preprocessing for single images —— ——
batch_transform_ops Data preprocessing for batch images —— ——

The parameter meaning of transform_ops:

Function name Parameter name Specific meaning
DecodeImage to_rgb data to RGB
channel_first image data by CHW
RandCropImage size Random crop
RandFlipImage Random flip
NormalizeImage scale Normalize scale value
mean Normalize mean value
std normalized variance
order Normalize order
CropImage size crop size
ResizeImage resize_short resize by short edge

The parameter meaning of batch_transform_ops:

Function name Parameter name Specific meaning
MixupOperator alpha Mixup parameter value，the larger the value, the stronger the augment
1.5.2 sampler
Parameter name Specific meaning Default value Optional value
name sampler type DistributedBatchSampler DistributedRandomIdentitySampler and other Sampler
batch_size batch size 64 int
drop_last Whether to drop the last data that does reach the batch-size False bool
shuffle whether to shuffle the data True bool
1.5.3 loader
Parameter name Specific meaning Default meaning Optional meaning
num_workers Number of data read threads 4 int
use_shared_memory Whether to use shared memory True bool

1.6 Evaluation metric

Parameter name Specific meaning Default meaning Optional meaning
TopkAcc TopkAcc [1, 5] list, int

1.7 Inference

Parameter name Specific meaning Default meaning Optional meaning
infer_imgs Image address to be inferred docs/images/whl/demo.jpg str
batch_size batch size 10 int
PostProcess.name Post-process name Topk str
PostProcess.topk topk value 5 int
PostProcess.class_id_map_file mapping file of class id and name ppcls/utils/imagenet1k_label_list.txt str

Note：The interpretation of transforms in the Infer module refers to the interpretation oftransform_opsin the dataset in the data reading module.

2.Distillation model

Note：Here the training configuration of MobileNetV3_large_x1_0 on ImageNet-1k distilled MobileNetV3_small_x1_0 is used as an example to explain the meaning of each parameter in detail. Configure path. Only parameters that are distinct from the classification model are introduced here.

2.1 Architecture

Parameter name Specific meaning Default meaning Optional meaning
name model arch name DistillationModel ——
class_num category number 1000 int
freeze_params_list freeze_params_list [True, False] list
models model list [Teacher, Student] list
Teacher.name teacher model name MobileNetV3_large_x1_0 PaddleClas model
Teacher.pretrained teacher model pre-trained weights True Boolean or pre-trained weight path
Teacher.use_ssld whether teacher model pretrained weights are ssld weights True Boolean
infer_model_name type of the model being inferred Student Teacher

Note：

  1. list is represented in yaml as follows:
  freeze_params_list:
  - True
  - False

2.Student's parameters are similar and will not be repeated.

2.2 Loss function

Parameter name Specific meaning Default meaning Optional meaning
DistillationCELoss Distillation's cross-entropy loss function —— ——
DistillationCELoss.weight Loss weight 1.0 float
DistillationCELoss.model_name_pairs ["Student", "Teacher"] —— ——
DistillationGTCELoss.weight Distillation's cross-entropy loss function of model and true Label —— ——
DistillationGTCELos.weight Loss weight 1.0 float
DistillationCELoss.model_names Model names with real label for cross-entropy ["Student"] ——

2.3 Evaluation metric

Parameter name Specific meaning Default meaning Optional meaning
DistillationTopkAcc DistillationTopkAcc including model_key and topk ——
DistillationTopkAcc.model_key the evaluated model "Student" "Teacher"
DistillationTopkAcc.topk Topk value [1, 5] list, int

Note： DistillationTopkAcc has the same meaning as TopkAcc, except that it is only used in distillation tasks.

3. Recognition model

Note：The training configuration ofResNet50 onLogoDet-3k is used here as an example to explain the meaning of each parameter in detail. configure path. Only parameters that are distinct from the classification model are presented here.

3.1 Architechture

Parameter name Specific meaning Default meaning Optional meaning
name Model arch "RecModel" ["RecModel"]
infer_output_key inference output value “feature” ["feature", "logits"]
infer_add_softmax softmaxwhether to add softmax to infercne False [True, False]
Backbone.name Backbone name ResNet50_last_stage_stride1 other backbone provided by PaddleClas
Backbone.pretrained Backbone pre-trained model True Boolean value or pre-trained model path
BackboneStopLayer.name The name of the output layer in Backbone True Thefull_nameof the feature output layer in Backbone
Neck.name The name of the Neck part VehicleNeck the dictionary structure to be passed in, the specific input parameters for the Neck network layer
Neck.in_channels Input dimension size of the Neck part 2048 the size is the same as BackboneStopLayer.name
Neck.out_channels Output the dimension size of the Neck part, i.e. feature dimension size 512 int
Head.name Network Head part nam CircleMargin Arcmargin. Etc
Head.embedding_size Feature dimension size 512 Consistent with Neck.out_channels
Head.class_num number of classes 3000 int
Head.margin margin value in CircleMargin 0.35 float
Head.scale scale value in CircleMargin 64 int

Note：

1.In PaddleClas, the Neck part is the connection part between Backbone and embedding layer, and Head part is the connection part between embedding layer and classification layer.。

2.BackboneStopLayer.name can be obtained by visualizing the model, visualization can be referred to Netron or visualdl.

3.Calling tools/export_model.py will convert the model weights to inference model, where the infer_add_softmax parameter will control whether to add the Softmax activation function afterwards, the code default is True (the last output layer in the classification task will be connected to the Softmax activation function). In the recognition task, the activation function is not required for the feature layer, so it should be set to False here.

3.2 Evaluation metric

Parameter name Specific meaning Default meaning Optional meaning
Recallk Recall rate [1, 5] list, int
mAP Average retrieval precision None None