mindspore/model_zoo/how_to_contribute/README_template.md

Title, Model name

The Description of Model. The paper present this model.

Model Architecture

There could be various architecture about some model. Represent the architecture of your implementation.

Features(optional)

Represent the distinctive feature you used in the model implementation. Such as distributed auto-parallel or some special training trick.

Dataset

Provide the information of the dataset you used. Check the copyrights of the dataset you used, usually don't provide the hyperlink to download the dataset.

Requirements

Provide details of the software required, including:

• The additional python package required. Add a requirements.txt file to the root dir of model for installing dependencies.
• The necessary third-party code.
• Some other system dependencies.
• Some additional operations before training or prediction.

Quick Start

How to take a try without understanding anything about the model.

Script Description

The section provide the detail of implementation.

Scripts and Sample Code

Explain every file in your project.

Script Parameter

Explain every parameter of the model. Especially the parameters in config.py.

Training

Provide training information.

Training Process

Provide the usage of training scripts.

e.g. Run the following command for distributed training on Ascend.

bash run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL]

Transfer Training(Optional)

Provide the guidelines about how to run transfer training based on an pretrained model.

Training Result

Provide the result of training.

e.g. Training checkpoint will be stored in XXXX/ckpt_0. You will get result from log file like the following:

epoch: 11 step: 7393 ,rpn_loss: 0.02003, rcnn_loss: 0.52051, rpn_cls_loss: 0.01761, rpn_reg_loss: 0.00241, rcnn_cls_loss: 0.16028, rcnn_reg_loss: 0.08411, rcnn_mask_loss: 0.27588, total_loss: 0.54054
epoch: 12 step: 7393 ,rpn_loss: 0.00547, rcnn_loss: 0.39258, rpn_cls_loss: 0.00285, rpn_reg_loss: 0.00262, rcnn_cls_loss: 0.08002, rcnn_reg_loss: 0.04990, rcnn_mask_loss: 0.26245, total_loss: 0.39804

Evaluation

Evaluation Process

Provide the use of evaluation scripts.

Evaluation Result

Provide the result of evaluation.

Performance

Training Performance

Provide the detail of training performance including finishing loss, throughput, checkpoint size and so on.

Inference Performance

Provide the detail of evaluation performance including latency, accuracy and so on.

Description of Random Situation

Explain the random situation in the project.

ModeZoo Homepage

Please check the official homepage.