# 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. ```shell 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](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).