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Running DeepLab on Cityscapes Semantic Segmentation Dataset

This page walks through the steps required to run DeepLab on Cityscapes on a local machine.

Download dataset and convert to TFRecord

We have prepared the script (under the folder datasets) to convert Cityscapes dataset to TFRecord. The users are required to download the dataset beforehand by registering the website.

# From the tensorflow/models/research/deeplab/datasets directory.
sh convert_cityscapes.sh

The converted dataset will be saved at ./deeplab/datasets/cityscapes/tfrecord.

Recommended Directory Structure for Training and Evaluation

+ datasets
  + cityscapes
    + leftImg8bit
    + gtFine
    + 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 Cityscapes train set).

Running the train/eval/vis jobs

A local training job using xception_65 can be run with the following command:

# From tensorflow/models/research/
python deeplab/train.py \
    --logtostderr \
    --training_number_of_steps=90000 \
    --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=769 \
    --train_crop_size=769 \
    --train_batch_size=1 \
    --dataset="cityscapes" \
    --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 Cityscapes dataset resides.

Note that for {train,eval,vis}.py:

  1. In order to reproduce our results, one needs to use large batch size (> 8), 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.

  4. Change and add the following flags in order to use the provided dense prediction cell.

--model_variant="xception_71"
--dense_prediction_cell_json="deeplab/core/dense_prediction_cell_branch5_top1_cityscapes.json"

A local evaluation job using xception_65 can be run with the following command:

# 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=1025 \
    --eval_crop_size=2049 \
    --dataset="cityscapes" \
    --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 Cityscapes dataset resides.

A local visualization job using xception_65 can be run with the following command:

# 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=1025 \
    --vis_crop_size=2049 \
    --dataset="cityscapes" \
    --colormap_type="cityscapes" \
    --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 Cityscapes 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:

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.