Content
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.
Dataset
Provide the information of the dataset you used. Check the copyrights of the dataset you used, usually you need to provide the hyperlink to download the dataset, scope and data size.
Features(optional)
Represent the distinctive feature you used in the model implementation. Such as distributed auto-parallel or some special training trick.
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.
Maybe include:
- run train,run eval,run export
- Ascend version, GPU version,CPU version
- offline version,ModelArts version
Script Description
The section provide the detail of implementation.
Scripts and Sample Code
Show the scope of project(include children directory), Explain every file in your project.
Script Parameter
Explain every parameter of the model. Especially the parameters in config.py
. If there are multiple config files, please explain separately.
Training
Provide training information. Include usage and log.
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]
Provide training logs.
# grep "loss is " train.log
epoch:1 step:390, loss is 1.4842823
epcoh:2 step:390, loss is 1.0897788
Provide training result.
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
Transfer Training(Optional)
Provide the guidelines about how to run transfer training based on an pretrained model.
Distribute Training
Same as Training
Evaluation
Evaluation Process 910
Provide the use of evaluation scripts.
Evaluation Result 910
Provide the result of evaluation.
Export
Export Process
Provide the use of export scripts.
Export Result
Provide the result of export.
Evaluation 310
Evaluation Process 310
Provide the use of evaluation scripts.
Evaluation Result 310
Provide the result of evaluation.
Performance
Training Performance
Provide the detail of training performance including finishing loss, throughput, checkpoint size and so on.
e.g. you can reference the following template
Parameters |
Ascend 910 |
GPU |
Model Version |
ResNet18 |
ResNet18 |
Resource |
Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 |
PCIE V100-32G |
uploaded Date |
02/25/2021 (month/day/year) |
07/23/2021 (month/day/year) |
MindSpore Version |
1.1.1 |
1.3.0 |
Dataset |
CIFAR-10 |
CIFAR-10 |
Training Parameters |
epoch=90, steps per epoch=195, batch_size = 32 |
epoch=90, steps per epoch=195, batch_size = 32 |
Optimizer |
Momentum |
Momentum |
Loss Function |
Softmax Cross Entropy |
Softmax Cross Entropy |
outputs |
probability |
probability |
Loss |
0.0002519517 |
0.0015517382 |
Speed |
13 ms/step(8pcs) |
29 ms/step(8pcs) |
Total time |
4 mins |
11 minds |
Parameters (M) |
11.2 |
11.2 |
Checkpoint for Fine tuning |
86M (.ckpt file) |
85.4 (.ckpt file) |
Scripts |
link |
|
Inference Performance
Provide the detail of evaluation performance including latency, accuracy and so on.
e.g. you can reference the following template
Parameters |
Ascend |
Model Version |
ResNet18 |
Resource |
Ascend 910; OS Euler2.8 |
Uploaded Date |
02/25/2021 (month/day/year) |
MindSpore Version |
1.1.1 |
Dataset |
CIFAR-10 |
batch_size |
32 |
outputs |
probability |
Accuracy |
94.02% |
Model for inference |
43M (.air file) |
Description of Random Situation
Explain the random situation in the project.
Reference Example
resnet_readme
Contributions
This part should not exist in your readme.
If you want to contribute, please review the contribution guidelines and how_to_contribute
Contributors
Update your school and email/gitee.
ModeZoo Homepage
Please check the official homepage.