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research/deeplab/g3doc/ade20k.md

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@@ -49,7 +49,7 @@ A local training job using `xception_65` can be run with the following command:
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# From tensorflow/models/research/
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python deeplab/train.py \
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--logtostderr \
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--training_number_of_steps=50000 \
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--training_number_of_steps=90000 \
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--train_split="train" \
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--model_variant="xception_65" \
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--atrous_rates=6 \
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--train_crop_size=513 \
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--train_crop_size=513 \
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--train_batch_size=4 \
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--min_resize_value=350 \
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--max_resize_value=500 \
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--min_resize_value=513 \
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--max_resize_value=513 \
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--resize_factor=16 \
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--fine_tune_batch_norm=False \
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--dataset="ade20k" \
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--initialize_last_layer=False \
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--last_layers_contain_logits_only=True \
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--tf_initial_checkpoint=${PATH_TO_INITIAL_CHECKPOINT} \
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--train_logdir=${PATH_TO_TRAIN_DIR}\
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--dataset_dir=${PATH_TO_DATASET}
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```
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where ${PATH\_TO\_INITIAL\_CHECKPOINT} is the path to the initial checkpoint.
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For example, if you are using the deeplabv3\_pascal\_train\_aug checkppoint, you
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will set this to `/path/to/deeplabv3\_pascal\_train\_aug/model.ckpt`.
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${PATH\_TO\_TRAIN\_DIR} is the directory in which training checkpoints and
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events will be written to (it is recommended to set it to the
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`train_on_train_set/train` above), and ${PATH\_TO\_DATASET} is the directory in
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4. The users could skip the flag, `decoder_output_stride`, if you do not want
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to use the decoder structure.
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Currently there are no fine-tuned checkpoint for the ADE20K dataset.
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## Running Tensorboard
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Progress for training and evaluation jobs can be inspected using Tensorboard. If

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