@ -71,10 +71,10 @@ After installing MindSpore via the official website, you can start training and
```python
# run training example
bash ./scripts/run_train_single .sh
bash ./scripts/run_standalone_train .sh
# run distributed training example
bash ./scripts/run_train.sh rank_table.json
bash ./scripts/run_distribute_ train.sh rank_table.json
# run evaluation example
python eval.py > eval.log 2>& 1 &
@ -97,10 +97,11 @@ For more details, please refer the specify script.
```bash
├── Tiny-DarkNet
├── README.md // descriptions about Tiny-Darknet
├── README.md // descriptions about Tiny-Darknet in English
├── README_CN.md // descriptions about Tiny-Darknet in Chinese
├── scripts
│ ├──run_train_single .sh // shell script for single on Ascend
│ ├──run_train.sh // shell script for distributed on Ascend
│ ├──run_standalone_train .sh // shell script for single on Ascend
│ ├──run_distribute_ train.sh // shell script for distributed on Ascend
│ ├──run_eval.sh // shell script for evaluation on Ascend
├── src
│ ├──dataset.py // creating dataset
@ -131,7 +132,6 @@ Parameters for both training and evaluation can be set in config.py
'data_path': './ImageNet_Original/train/' # absolute full path to the train datasets
'val_data_path': './ImageNet_Original/val/' # absolute full path to the evaluation datasets
'device_target': 'Ascend' # device running the program
'device_id': 0 # device ID used to train or evaluate the dataset. Ignore it when you use run_train.sh for distributed training
'keep_checkpoint_max': 10 # only keep the last keep_checkpoint_max checkpoint
'checkpoint_path': '/train_tinydarknet.ckpt' # the absolute full path to save the checkpoint file
'onnx_filename': 'tinydarknet.onnx' # file name of the onnx model used in export.py
@ -157,7 +157,7 @@ For more configuration details, please refer the script config.py.
- running on Ascend：
```python
sh scripts/run_train_single .sh
bash scripts/run_standalone_train .sh
```
The command above will run in the background, you can view the results through the file train.log.
@ -184,7 +184,7 @@ For more configuration details, please refer the script config.py.
- running on Ascend：
```python
sh scripts/run_train.sh
ba sh ./ scripts/run_distribute_ train.sh rank_table.json
```
The above shell script will run distribute training in the background. You can view the results through the file train_parallel[X]/log. The loss value will be achieved as follows:
@ -211,7 +211,7 @@ For more configuration details, please refer the script config.py.
```python
python eval.py > eval.log 2>& 1 &
OR
sh scripts/run_eval.sh
ba sh scripts/run_eval.sh
```
The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
@ -232,7 +232,7 @@ For more configuration details, please refer the script config.py.
## [Performance ](#contents )
### [Evaluation Performance](#contents )
### [Training Performance](#contents )
| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
@ -260,7 +260,7 @@ For more configuration details, please refer the script config.py.
| Dataset | 200k images |
| batch_size | 128 |
| Outputs | probability |
| Accuracy | 8 pc Top-5: 81.7% |
| Accuracy | 8 pc Top-1: 58.7%; Top- 5: 81.7% |
| Model for inference | 11.6M (.ckpt file) |
# [ModelZoo Homepage ](#contents )
wukesong
5 years ago