configs

File Structure and Naming

This folder contains training recipes and model readme files for each model. The folder structure and naming rule of model configurations are as follows.

    ├── configs
        ├── model_a                         // model name in lower case with _ seperator
        │   ├─ model_a_small_ascend.yaml    // training recipe denated as {model_name}_{specification}_{hardware}.yaml
        |   ├─ model_a_large_gpu.yaml
        │   ├─ README.md                    //readme file containing performance results and pretrained weight urls
        │   └─ README_CN.md                 //readme file in Chinese
        ├── model_b
        │   ├─ model_b_32_ascend.yaml
        |   ├─ model_l_16_ascend.yaml
        │   ├─ README.md
        │   └─ README_CN.md
        ├── README.md //this file

Note: Our training recipes are verified on specific hardware, and the suffix hardware (ascend or gpu) in the file name of training recipes indicates different hardware. Since Mindspore operators have different precision and performance on different hardware, different training recipes are required under different hardware. However, if you want to train on another hardware (e.g. GPU) using the training recipe under specific hardware (e.g. Ascend), you only need to make minor or no adjustments to the hyperparameters, because the training recipe has a certain degree of generalization across different hardware.

Model Readme Writing Guideline

The model readme file in each sub-folder provides the introduction, reproduced results, and running guideline for each model.

Please follow the outline structure and table format shown in densenet/README.md when contributing your models :)

Table Format

Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Recipe	Download
densenet_121	D910x8-G	75.64	92.84	8.06	yaml	weights

Illustration:

• Model: model name in lower case with _ seperator.
• Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
• Top-1 and Top-5: Accuracy reported on the validatoin set of ImageNet-1K. Keep 2 digits after the decimal point.
• Params (M): # of model parameters in millions (10^6). Keep 2 digits after the decimal point
• Recipe: Training recipe/configuration linked to a yaml config file.
• Download: url of the pretrained model weights

Model Checkpoint Format

The checkpoint (i.e., model weight) name should follow this format: {model_name}_{specification}-{sha256sum}.ckpt, e.g., poolformer_s12-5be5c4e4.ckpt.

You can run the following command and take the first 8 characters of the computing result as the sha256sum value in the checkpoint name.

sha256sum your_model.ckpt

Training Script Format

For consistency, it is recommended to provide distributed training commands based on mpirun -n {num_devices} python train.py, instead of using shell script such as distrubuted_train.sh.

# standalone training on a gpu or ascend device
python train.py --config configs/densenet/densenet_121_gpu.yaml --data_dir /path/to/dataset --distribute False

# distributed training on gpu or ascend divices
mpirun -n 8 python train.py --config configs/densenet/densenet_121_ascend.yaml --data_dir /path/to/imagenet

If the script is executed by the root user, the --allow-run-as-root parameter must be added to mpirun.

URL and Hyperlink Format

Please use absolute path in the hyperlink or url for linking the target resource in the readme file and table.