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` on`ImageNet-1k`is used as an example to explain the each parameter in detail. [Configure Path](https://github.com/PaddlePaddle/PaddleClas/blob/develop/ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml).
#### 1.1Global Configuration
| Parameter name | Specific meaning | Defult value | Optional value |
**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 |
**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](https://github.com/PaddlePaddle/PaddleClas/blob/develop/ppcls/optimizer/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 |
| 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 of`transform_ops`in 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](https://github.com/PaddlePaddle/PaddleClas/blob/develop/ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml). Only parameters that are distinct from the classification model are introduced here.
#### 2.1 Architecture
| 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 of`ResNet50` on`LogoDet-3k` is used here as an example to explain the meaning of each parameter in detail. [configure path](https://github.com/PaddlePaddle/PaddleClas/blob/develop/ppcls/configs/Logo/ResNet50_ReID.yaml). Only parameters that are distinct from the classification model are presented here.
#### 3.1 Architechture
| Parameter name | Specific meaning | Default meaning | Optional meaning |
| 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 | The`full_name`of 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.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](https://github.com/lutzroeder/netron) or [visualdl](https://github.com/PaddlePaddle/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 |
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 |
