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# Deeplab-V3 Example
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## Description
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This is an example of training DeepLabv3 with PASCAL VOC 2012 dataset in MindSpore.
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## Requirements
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- Install [MindSpore](https://www.mindspore.cn/install/en).
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- Download the VOC 2012 dataset for training.
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> Notes:
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If you are running a fine-tuning or evaluation task, prepare the corresponding checkpoint file.
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## Running the Example
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### Training
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- Set options in config.py.
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- Run `run_standalone_train.sh` for non-distributed training.
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``` bash
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sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
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```
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- Run `run_distribute_train.sh` for distributed training.
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``` bash
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sh scripts/run_distribute_train.sh MINDSPORE_HCCL_CONFIG_PATH DATA_PATH
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```
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### Evaluation
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Set options in evaluation_config.py. Make sure the 'data_file' and 'finetune_ckpt' are set to your own path.
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- Run run_eval.sh for evaluation.
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``` bash
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sh scripts/run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH
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```
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## Options and Parameters
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It contains of parameters of Deeplab-V3 model and options for training, which is set in file config.py.
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### Options:
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```
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config.py:
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learning_rate Learning rate, default is 0.0014.
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weight_decay Weight decay, default is 5e-5.
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momentum Momentum, default is 0.97.
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crop_size Image crop size [height, width] during training, default is 513.
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eval_scales The scales to resize images for evaluation, default is [0.5, 0.75, 1.0, 1.25, 1.5, 1.75].
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output_stride The ratio of input to output spatial resolution, default is 16.
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ignore_label Ignore label value, default is 255.
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seg_num_classes Number of semantic classes, including the background class (if exists).
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foreground classes + 1 background class in the PASCAL VOC 2012 dataset, default is 21.
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fine_tune_batch_norm Fine tune the batch norm parameters or not, default is False.
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atrous_rates Atrous rates for atrous spatial pyramid pooling, default is None.
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decoder_output_stride The ratio of input to output spatial resolution when employing decoder
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to refine segmentation results, default is None.
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image_pyramid Input scales for multi-scale feature extraction, default is None.
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epoch_size Epoch size, default is 6.
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batch_size batch size of input dataset: N, default is 2.
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enable_save_ckpt Enable save checkpoint, default is true.
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save_checkpoint_steps Save checkpoint steps, default is 1000.
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save_checkpoint_num Save checkpoint numbers, default is 1.
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```
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### Parameters:
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```
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Parameters for dataset and network:
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distribute Run distribute, default is false.
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data_url Train/Evaluation data url, required.
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checkpoint_url Checkpoint path, default is None.
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```
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