mmpretrain/docs/en/advanced_guides/convention.md

Convention in MMCLS

Config files 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_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py

• 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.

Checkpoint Naming Convention

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

{config_name}_{date}-{hash}.pth