Tutorial 1: Learn about Configs

MMClassification mainly uses python files as configs. The design of our configuration file system integrates modularity and inheritance, facilitating users to conduct various experiments. All configuration files are placed in the configs folder, which mainly contains the primitive configuration folder of _base_ and many algorithm folders such as resnet, swin_transformer, vision_transformer, etc.

If you wish to inspect the config file, you may run python tools/misc/ /PATH/TO/CONFIG to see the complete config.

Config File and Checkpoint Naming Convention

We follow the below convention to name config files. Contributors are advised to follow the same style. The config file names are divided into four parts: algorithm info, module information, training information and data information. Logically, different parts are concatenated by underscores '_', and words in the same part are concatenated by dashes '-'.

{algorithm info}_{module info}_{training info}_{data info}.py
  • algorithm info:algorithm information, model name and neural network architecture, such as resnet, etc.;

  • module info: module information is used to represent some special neck, head and pretrain information;

  • training info:Training information, some training schedule, including batch size, lr schedule, data augment and the like;

  • data info:Data information, dataset name, input size and so on, such as imagenet, cifar, etc.;

Algorithm information

The main algorithm name and the corresponding branch architecture information. E.g:

  • resnet50

  • mobilenet-v3-large

  • vit-small-patch32 : patch32 represents the size of the partition in ViT algorithm;

  • seresnext101-32x4d : SeResNet101 network structure, 32x4d means that groups and width_per_group are 32 and 4 respectively in Bottleneck;

Module information

Some special neck, head and pretrain information. In classification tasks, pretrain information is the most commonly used:

  • in21k-pre : pre-trained on ImageNet21k;

  • in21k-pre-3rd-party : pre-trained on ImageNet21k and the checkpoint is converted from a third-party repository;

Training information

Training schedule, including training type, batch size, lr schedule, data augment, special loss functions and so on:

  • format {gpu x batch_per_gpu}, such as 8xb32

Training type (mainly seen in the transformer network, such as the ViT algorithm, which is usually divided into two training type: pre-training and fine-tuning):

  • ft : configuration file for fine-tuning

  • pt : configuration file for pretraining

Training recipe. Usually, only the part that is different from the original paper will be marked. These methods will be arranged in the order {pipeline aug}-{train aug}-{loss trick}-{scheduler}-{epochs}.

  • coslr-200e : use cosine scheduler to train 200 epochs

  • autoaug-mixup-lbs-coslr-50e : use autoaug, mixup, label smooth, cosine scheduler to train 50 epochs

Data information

  • in1k : ImageNet1k dataset, default to use the input image size of 224x224;

  • in21k : ImageNet21k dataset, also called ImageNet22k dataset, default to use the input image size of 224x224;

  • in1k-384px : Indicates that the input image size is 384x384;

  • cifar100

Config File Name Example
  • repvgg-D2se: Algorithm information

    • repvgg: The main algorithm.

    • D2se: The architecture.

  • deploy: Module information, means the backbone is in the deploy state.

  • 4xb64-autoaug-lbs-mixup-coslr-200e: Training information.

    • 4xb64: Use 4 GPUs and the size of batches per GPU is 64.

    • autoaug: Use AutoAugment in training pipeline.

    • lbs: Use label smoothing loss.

    • mixup: Use mixup training augment method.

    • coslr: Use cosine learning rate scheduler.

    • 200e: Train the model for 200 epochs.

  • in1k: Dataset information. The config is for ImageNet1k dataset and the input size is 224x224.


Some configuration files currently do not follow this naming convention, and related files will be updated in the near future.

Checkpoint Naming Convention

The naming of the weight mainly includes the configuration file name, date and hash value.


Config File Structure

There are four kinds of basic component file in the configs/_base_ folders, namely:

You can easily build your own training config file by inherit some base config files. And the configs that are composed by components from _base_ are called primitive.

For easy understanding, we use ResNet50 primitive config as a example and comment the meaning of each line. For more detaile, please refer to the API documentation.

_base_ = [
    '../_base_/models/',           # model
    '../_base_/datasets/',    # data
    '../_base_/schedules/',  # training schedule
    '../_base_/'            # runtime setting

The four parts are explained separately below, and the above-mentioned ResNet50 primitive config are also used as an example.


The parameter "model" is a python dictionary in the configuration file, which mainly includes information such as network structure and loss function:

  • type : Classifier name, MMCls supports ImageClassifier, refer to API documentation.

  • backbone : Backbone configs, refer to API documentation for available options.

  • neck :Neck network name, MMCls supports GlobalAveragePooling, please refer to API documentation.

  • head: Head network name, MMCls supports single-label and multi-label classification head networks, available options refer to API documentation.

  • train_cfg :Training augment config, MMCls supports mixup, cutmix and other augments.


The ‘type’ in the configuration file is not a constructed parameter, but a class name.

model = dict(
    type='ImageClassifier',     # Classifier name
        type='ResNet',          # Backbones name
        depth=50,               # depth of backbone, ResNet has options of 18, 34, 50, 101, 152.
        num_stages=4,           # number of stages,The feature maps generated by these states are used as the input for the subsequent neck and head.
        out_indices=(3, ),      # The output index of the output feature maps.
        frozen_stages=-1,       # the stage to be frozen, '-1' means not be forzen
        style='pytorch'),        # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs.
    neck=dict(type='GlobalAveragePooling'),    # neck network name
        type='LinearClsHead',     # linear classification head,
        num_classes=1000,         # The number of output categories, consistent with the number of categories in the dataset
        in_channels=2048,         # The number of input channels, consistent with the output channel of the neck
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0), # Loss function configuration information
        topk=(1, 5),              # Evaluation index, Top-k accuracy rate, here is the accuracy rate of top1 and top5


The parameter "data" is a python dictionary in the configuration file, which mainly includes information to construct dataloader:

  • samples_per_gpu : the BatchSize of each GPU when building the dataloader

  • workers_per_gpu : the number of threads per GPU when building dataloader

  • train val test : config to construct dataset

    • type: Dataset name, MMCls supports ImageNet, Cifar etc., refer to API documentation

    • data_prefix : Dataset root directory

    • pipeline : Data processing pipeline, refer to related tutorial CUSTOM DATA PIPELINES

The parameter evaluation is also a dictionary, which is the configuration information of evaluation hook, mainly including evaluation interval, evaluation index, etc..

# dataset settings
dataset_type = 'ImageNet'  # dataset name,
img_norm_cfg = dict(        # Image normalization config to normalize the input images
    mean=[123.675, 116.28, 103.53],  # Mean values used to pre-training the pre-trained backbone models
    std=[58.395, 57.12, 57.375],     # Standard variance used to pre-training the pre-trained backbone models
    to_rgb=True)                     # Whether to invert the color channel, rgb2bgr or bgr2rgb.
# train data pipeline
train_pipeline = [
    dict(type='LoadImageFromFile'),                # First pipeline to load images from file path
    dict(type='RandomResizedCrop', size=224),      # RandomResizedCrop
    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),  # Randomly flip the picture horizontally with a probability of 0.5
    dict(type='Normalize', **img_norm_cfg),        # normalization
    dict(type='ImageToTensor', keys=['img']),      # convert image from numpy into torch.Tensor
    dict(type='ToTensor', keys=['gt_label']),      # convert gt_label into torch.Tensor
    dict(type='Collect', keys=['img', 'gt_label']) # Pipeline that decides which keys in the data should be passed to the detector
# test data pipeline
test_pipeline = [
    dict(type='Resize', size=(256, -1)),
    dict(type='CenterCrop', crop_size=224),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='Collect', keys=['img'])             # do not pass gt_label while testing
data = dict(
    samples_per_gpu=32,     # Batch size of a single GPU
    workers_per_gpu=2,      # Worker to pre-fetch data for each single GPU
    train=dict(  # Train dataset config
    train=dict(            # train data config
        type=dataset_type,                  # dataset name
        data_prefix='data/imagenet/train',  # Dataset root, when ann_file does not exist, the category information is automatically obtained from the root folder
        pipeline=train_pipeline),           # train data pipeline
    val=dict(              # val data config
        ann_file='data/imagenet/meta/val.txt',   #  ann_file existes, the category information is obtained from file
    test=dict(             # test data config
evaluation = dict(       # The config to build the evaluation hook, refer to for more details.
    interval=1,          # Evaluation interval
    metric='accuracy')   # Metrics used during evaluation

training schedule

Mainly include optimizer settings, optimizer hook settings, learning rate schedule and runner settings:

  • optimizer: optimizer setting , support all optimizers in pytorch, refer to related mmcv documentation.

  • optimizer_config: optimizer hook configuration file, such as setting gradient limit, refer to related mmcv code.

  • lr_config: Learning rate scheduler, supports “CosineAnnealing”, “Step”, “Cyclic”, etc. refer to related mmcv documentation for more options.

  • runner: For runner, please refer to mmcv for runner introduction document.

# he configuration file used to build the optimizer, support all optimizers in PyTorch.
optimizer = dict(type='SGD',         # Optimizer type
                lr=0.1,              # Learning rate of optimizers, see detail usages of the parameters in the documentation of PyTorch
                momentum=0.9,        # Momentum
                weight_decay=0.0001) # Weight decay of SGD
# Config used to build the optimizer hook, refer to for implementation details.
optimizer_config = dict(grad_clip=None)  # Most of the methods do not use gradient clip
# Learning rate scheduler config used to register LrUpdater hook
lr_config = dict(policy='step',          # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from
                 step=[30, 60, 90])      # Steps to decay the learning rate
runner = dict(type='EpochBasedRunner',   # Type of runner to use (i.e. IterBasedRunner or EpochBasedRunner)
            max_epochs=100)    # Runner that runs the workflow in total max_epochs. For IterBasedRunner use `max_iters`

runtime setting

This part mainly includes saving the checkpoint strategy, log configuration, training parameters, breakpoint weight path, working directory, etc..

# Config to set the checkpoint hook, Refer to for implementation.
checkpoint_config = dict(interval=1)    # The save interval is 1
# config to register logger hook
log_config = dict(
    interval=100,                       # Interval to print the log
        dict(type='TextLoggerHook'),           # The Tensorboard logger is also supported
        # dict(type='TensorboardLoggerHook')

dist_params = dict(backend='nccl')   # Parameters to setup distributed training, the port can also be set.
log_level = 'INFO'             # The output level of the log.
resume_from = None             # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
workflow = [('train', 1)]      # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once.
work_dir = 'work_dir'          # Directory to save the model checkpoints and logs for the current experiments.

Inherit and Modify Config File

For easy understanding, we recommend contributors to inherit from existing methods.

For all configs under the same folder, it is recommended to have only one primitive config. All other configs should inherit from the primitive config. In this way, the maximum of inheritance level is 3.

For example, if your config file is based on ResNet with some other modification, you can first inherit the basic ResNet structure, dataset and other training setting by specifying _base_ ='./' (The path relative to your config file), and then modify the necessary parameters in the config file. A more specific example, now we want to use almost all configs in configs/resnet/, but change the number of training epochs from 100 to 300, modify when to decay the learning rate, and modify the dataset path, you can create a new config file configs/resnet/ with content as below:

_base_ = './'

runner = dict(max_epochs=300)
lr_config = dict(step=[150, 200, 250])

data = dict(
    val=dict(data_prefix='mydata/imagenet/train', ),
    test=dict(data_prefix='mydata/imagenet/train', )

Use intermediate variables in configs

Some intermediate variables are used in the configuration file. The intermediate variables make the configuration file clearer and easier to modify.

For example, train_pipeline / test_pipeline is the intermediate variable of the data pipeline. We first need to define train_pipeline / test_pipeline, and then pass them to data. If you want to modify the size of the input image during training and testing, you need to modify the intermediate variables of train_pipeline / test_pipeline.

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='RandomResizedCrop', size=384, backend='pillow',),
    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='ToTensor', keys=['gt_label']),
    dict(type='Collect', keys=['img', 'gt_label'])
test_pipeline = [
    dict(type='Resize', size=384, backend='pillow'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='Collect', keys=['img'])
data = dict(

Ignore some fields in the base configs

Sometimes, you need to set _delete_=True to ignore some domain content in the basic configuration file. You can refer to mmcv for more instructions.

The following is an example. If you wangt to use cosine schedule in the above ResNet50 case, just using inheritance and directly modify it will report get unexcepected keyword'step' error, because the 'step' field of the basic config in lr_config domain information is reserved, and you need to add _delete_ =True to ignore the content of lr_config related fields in the basic configuration file:

_base_ = '../../configs/resnet/'

lr_config = dict(

Use some fields in the base configs

Sometimes, you may refer to some fields in the _base_ config, so as to avoid duplication of definitions. You can refer to mmcv for some more instructions.

The following is an example of using auto augment in the training data preprocessing pipeline, refer to configs/_base_/datasets/ When defining train_pipeline, just add the definition file name of auto augment to _base_, and then use {{_base_.auto_increasing_policies}} to reference the variables:

_base_ = ['./pipelines/']

# dataset settings
dataset_type = 'ImageNet'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='RandomResizedCrop', size=224),
    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
    dict(type='AutoAugment', policies={{_base_.auto_increasing_policies}}),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='ToTensor', keys=['gt_label']),
    dict(type='Collect', keys=['img', 'gt_label'])
test_pipeline = [...]
data = dict(
    train=dict(..., pipeline=train_pipeline),
    val=dict(..., pipeline=test_pipeline))
evaluation = dict(interval=1, metric='accuracy')

Modify config through script arguments

When users use the script “tools/” or “tools/” to submit tasks or use some other tools, they can directly modify the content of the configuration file used by specifying the --cfg-options parameter.

  • Update config keys of dict chains.

    The config options can be specified following the order of the dict keys in the original config. For example, --cfg-options model.backbone.norm_eval=False changes the all BN modules in model backbones to train mode.

  • Update keys inside a list of configs.

    Some config dicts are composed as a list in your config. For example, the training pipeline data.train.pipeline is normally a list e.g. [dict(type='LoadImageFromFile'), dict(type='TopDownRandomFlip', flip_prob=0.5), ...]. If you want to change 'flip_prob=0.5' to 'flip_prob=0.0' in the pipeline, you may specify --cfg-options data.train.pipeline.1.flip_prob=0.0.

  • Update values of list/tuples.

    If the value to be updated is a list or a tuple. For example, the config file normally sets workflow=[('train', 1)]. If you want to change this key, you may specify --cfg-options workflow="[(train,1),(val,1)]". Note that the quotation mark ” is necessary to support list/tuple data types, and that NO white space is allowed inside the quotation marks in the specified value.

Import user-defined modules


This part may only be used when using MMClassification as a third party library to build your own project, and beginners can skip it.

After studying the follow-up tutorials ADDING NEW DATASET, CUSTOM DATA PIPELINES, ADDING NEW MODULES. You may use MMClassification to complete your project and create new classes of datasets, models, data enhancements, etc. in the project. In order to streamline the code, you can use MMClassification as a third-party library, you just need to keep your own extra code and import your own custom module in the configuration files. For examples, you may refer to OpenMMLab Algorithm Competition Project .

Add the following code to your own configuration files:

custom_imports = dict(


  • None

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