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# Running DeepLab on PASCAL VOC 2012 Semantic Segmentation Dataset
This page walks through the steps required to run DeepLab on PASCAL VOC 2012 on
a local machine.
## Download dataset and convert to TFRecord
We have prepared the script (under the folder `datasets`) to download and
convert PASCAL VOC 2012 semantic segmentation dataset to TFRecord.
```bash
# From the tensorflow/models/research/deeplab/datasets directory.
sh download_and_convert_voc2012.sh
```
The converted dataset will be saved at
./deeplab/datasets/pascal_voc_seg/tfrecord
## Recommended Directory Structure for Training and Evaluation
```
+ datasets
+ pascal_voc_seg
+ VOCdevkit
+ VOC2012
+ JPEGImages
+ SegmentationClass
+ tfrecord
+ exp
+ train_on_train_set
+ train
+ eval
+ vis
```
where the folder `train_on_train_set` stores the train/eval/vis events and
results (when training DeepLab on the PASCAL VOC 2012 train set).
## Running the train/eval/vis jobs
A local training job using `xception_65` can be run with the following command:
```bash
# From tensorflow/models/research/
python deeplab/train.py \
--logtostderr \
--training_number_of_steps=30000 \
--train_split="train" \
--model_variant="xception_65" \
--atrous_rates=6 \
--atrous_rates=12 \
--atrous_rates=18 \
--output_stride=16 \
--decoder_output_stride=4 \
--train_crop_size="513,513" \
--train_batch_size=1 \
--dataset="pascal_voc_seg" \
--tf_initial_checkpoint=${PATH_TO_INITIAL_CHECKPOINT} \
--train_logdir=${PATH_TO_TRAIN_DIR} \
--dataset_dir=${PATH_TO_DATASET}
```
where ${PATH_TO_INITIAL_CHECKPOINT} is the path to the initial checkpoint
(usually an ImageNet pretrained checkpoint), ${PATH_TO_TRAIN_DIR} is the
directory in which training checkpoints and events will be written to, and
${PATH_TO_DATASET} is the directory in which the PASCAL VOC 2012 dataset
resides.
**Note that for {train,eval,vis}.py:**
1. In order to reproduce our results, one needs to use large batch size (> 12),
and set fine_tune_batch_norm = True. Here, we simply use small batch size
during training for the purpose of demonstration. If the users have limited
GPU memory at hand, please fine-tune from our provided checkpoints whose
batch norm parameters have been trained, and use smaller learning rate with
fine_tune_batch_norm = False.
2. The users should change atrous_rates from [6, 12, 18] to [12, 24, 36] if
setting output_stride=8.
3. The users could skip the flag, `decoder_output_stride`, if you do not want
to use the decoder structure.
A local evaluation job using `xception_65` can be run with the following
command:
```bash
# From tensorflow/models/research/
python deeplab/eval.py \
--logtostderr \
--eval_split="val" \
--model_variant="xception_65" \
--atrous_rates=6 \
--atrous_rates=12 \
--atrous_rates=18 \
--output_stride=16 \
--decoder_output_stride=4 \
--eval_crop_size="513,513" \
--dataset="pascal_voc_seg" \
--checkpoint_dir=${PATH_TO_CHECKPOINT} \
--eval_logdir=${PATH_TO_EVAL_DIR} \
--dataset_dir=${PATH_TO_DATASET}
```
where ${PATH_TO_CHECKPOINT} is the path to the trained checkpoint (i.e., the
path to train_logdir), ${PATH_TO_EVAL_DIR} is the directory in which evaluation
events will be written to, and ${PATH_TO_DATASET} is the directory in which the
PASCAL VOC 2012 dataset resides.
A local visualization job using `xception_65` can be run with the following
command:
```bash
# From tensorflow/models/research/
python deeplab/vis.py \
--logtostderr \
--vis_split="val" \
--model_variant="xception_65" \
--atrous_rates=6 \
--atrous_rates=12 \
--atrous_rates=18 \
--output_stride=16 \
--decoder_output_stride=4 \
--vis_crop_size="513,513" \
--dataset="pascal_voc_seg" \
--checkpoint_dir=${PATH_TO_CHECKPOINT} \
--vis_logdir=${PATH_TO_VIS_DIR} \
--dataset_dir=${PATH_TO_DATASET}
```
where ${PATH_TO_CHECKPOINT} is the path to the trained checkpoint (i.e., the
path to train_logdir), ${PATH_TO_VIS_DIR} is the directory in which evaluation
events will be written to, and ${PATH_TO_DATASET} is the directory in which the
PASCAL VOC 2012 dataset resides. Note that if the users would like to save the
segmentation results for evaluation server, set also_save_raw_predictions =
True.
## Running Tensorboard
Progress for training and evaluation jobs can be inspected using Tensorboard. If
using the recommended directory structure, Tensorboard can be run using the
following command:
```bash
tensorboard --logdir=${PATH_TO_LOG_DIRECTORY}
```
where `${PATH_TO_LOG_DIRECTORY}` points to the directory that contains the
train, eval, and vis directories (e.g., the folder `train_on_train_set` in the
above example). Please note it may take Tensorboard a couple minutes to populate
with data.
## Example
We provide a script to run the {train,eval,vis,export_model}.py on the PASCAL VOC
2012 dataset as an example. See the code in local_test.sh for details.
```bash
# From tensorflow/models/research/deeplab
sh local_test.sh
```
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