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0e3e8e91cc
* refactor config doc * refine * Fix the config docs. * update CN config * Fix some grammar erros. * Fix Chinese docs. Co-authored-by: mzr1996 <mzr1996@163.com>
81 lines
3.6 KiB
Markdown
81 lines
3.6 KiB
Markdown
# Convention in MMCLS
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## Config files Naming Convention
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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 `'-'`.
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```text
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{algorithm info}_{module info}_{training info}_{data info}.py
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```
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- `algorithm info`：algorithm information, model name and neural network architecture, such as resnet, etc.;
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- `module info`： module information is used to represent some special neck, head and pretrain information;
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- `training info`：Training information, some training schedule, including batch size, lr schedule, data augment and the like;
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- `data info`：Data information, dataset name, input size and so on, such as imagenet, cifar, etc.;
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### Algorithm information
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The main algorithm name and the corresponding branch architecture information. E.g：
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- `resnet50`
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- `mobilenet-v3-large`
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- `vit-small-patch32` : `patch32` represents the size of the partition in `ViT` algorithm;
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- `seresnext101-32x4d` : `SeResNet101` network structure, `32x4d` means that `groups` and `width_per_group` are 32 and 4 respectively in `Bottleneck`;
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### Module information
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Some special `neck`, `head` and `pretrain` information. In classification tasks, `pretrain` information is the most commonly used:
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- `in21k-pre` : pre-trained on ImageNet21k;
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- `in21k-pre-3rd-party` : pre-trained on ImageNet21k and the checkpoint is converted from a third-party repository;
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### Training information
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Training schedule, including training type, `batch size`, `lr schedule`, data augment, special loss functions and so on:
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- format `{gpu x batch_per_gpu}`, such as `8xb32`
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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):
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- `ft` : configuration file for fine-tuning
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- `pt` : configuration file for pretraining
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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}`.
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- `coslr-200e` : use cosine scheduler to train 200 epochs
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- `autoaug-mixup-lbs-coslr-50e` : use `autoaug`, `mixup`, `label smooth`, `cosine scheduler` to train 50 epochs
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### Data information
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- `in1k` : `ImageNet1k` dataset, default to use the input image size of 224x224;
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- `in21k` : `ImageNet21k` dataset, also called `ImageNet22k` dataset, default to use the input image size of 224x224;
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- `in1k-384px` : Indicates that the input image size is 384x384;
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- `cifar100`
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### Config File Name Example
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```text
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repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py
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```
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- `repvgg-D2se`: Algorithm information
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- `repvgg`: The main algorithm.
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- `D2se`: The architecture.
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- `deploy`: Module information, means the backbone is in the deploy state.
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- `4xb64-autoaug-lbs-mixup-coslr-200e`: Training information.
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- `4xb64`: Use 4 GPUs and the size of batches per GPU is 64.
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- `autoaug`: Use `AutoAugment` in training pipeline.
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- `lbs`: Use label smoothing loss.
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- `mixup`: Use `mixup` training augment method.
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- `coslr`: Use cosine learning rate scheduler.
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- `200e`: Train the model for 200 epochs.
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- `in1k`: Dataset information. The config is for `ImageNet1k` dataset and the input size is `224x224`.
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## Checkpoint Naming Convention
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The naming of the weight mainly includes the configuration file name, date and hash value.
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```text
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{config_name}_{date}-{hash}.pth
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```
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