TF-Slim models
Project description
Models
######
images
######
*Generic images dataset*
Operations
==========
prepare
^^^^^^^
*Prepare images for training*
Flags
-----
**images**
*Directory containing images to prepare (required)*
**random-seed**
*Seed used for train/validation split (randomly generated)*
**val-split**
*Percentage of images reserved for validation (30)*
inception
#########
*TF-Slim Inception v1 classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
inception-resnet-v2
###################
*TF-Slim Inception ResNet v2 classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
inception-v2
############
*TF-Slim Inception v2 classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
inception-v3
############
*TF-Slim Inception v3 classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
inception-v4
############
*TF-Slim Inception v4 classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
mobilenet
#########
*TF-Slim Mobilenet v1 classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
mobilenet-v2-1.4
################
*TF-Slim Mobilenet v2 classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
nasnet-large
############
*TF-Slim NASNet large classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
nasnet-mobile
#############
*TF-Slim NASNet mobile classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
pnasnet-large
#############
*TF-Slim PNASNet classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
pnasnet-mobile
##############
*TF-Slim PNASNet mobile classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
resnet-101
##########
*TF-Slim ResNet v1 101 layer classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
resnet-152
##########
*TF-Slim ResNet v1 152 layer classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
resnet-50
#########
*TF-Slim ResNet v1 50 layer classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
resnet-v2-101
#############
*TF-Slim ResNet v2 101 layer classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
resnet-v2-152
#############
*TF-Slim ResNet v2 152 layer classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
resnet-v2-50
############
*TF-Slim ResNet v2 50 layer classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
vgg-16
######
*TF-Slim VGG 16 classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
vgg-19
######
*TF-Slim VGG 19 classifier*
Operations
==========
evaluate
^^^^^^^^
*Evaluate a trained model*
Flags
-----
**batch-size**
*Number of examples in each evaluated batch (100)*
**eval-batches**
*Number of batches to evaluate (all available)*
**step**
*Checkpoint step to evaluate (latest checkpoint)*
export-and-freeze
^^^^^^^^^^^^^^^^^
*Export an inference graph with checkpoint weights*
Flags
-----
**step**
*Checkpoint step to use for the frozen graph (latest checkpoint)*
finetune
^^^^^^^^
*Finetune a trained model*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.0001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
label
^^^^^
*Classify an image using a trained model*
Flags
-----
**image**
*Path to image to classify (required)*
tflite
^^^^^^
*Generate a TFLite file from a frozen graph*
train
^^^^^
*Train model from scratch*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
transfer-learn
^^^^^^^^^^^^^^
*Train model using transfer learning*
Flags
-----
**auto-scale**
*Adjust applicable flags for multi-GPU systems (yes)
Set to 'no' to disable any flag value adjustments.
When this value is 'yes' (the default) the following flags are adjusted on
multi-GPU systems:
- clones
- learning-rate
`clones` is set to the number of available GPUs.
`learning-rate` is adjusted by multiplying its specified value by the
number of GPUs.
Flags are not adjusted on single GPU or CPU only systems.
*
**batch-size**
*Number of examples in each training batch (32)*
**clones**
*Number of model clones (calculated)
This value is automatically set to the number of available GPUs if `auto-
scale` is 'yes'.
When `auto-scale` is 'no' this value can be increased from 1 to train the
model in parallel on multiple GPUs.
*
**learning-rate**
*Initial learning rate (0.001)*
**learning-rate-decay-epochs**
*Number of epochs after which learning rate decays (2.0)*
**learning-rate-decay-factor**
*Learning rate decay factor (0.94)*
**learning-rate-decay-type**
*Method used to decay the learning rate (exponential)
Choices:
exponential
fixed
polynomial
*
**learning-rate-end**
*Minimal learning rate used by polynomial learning rate decay (0.0001)*
**log-save-seconds**
*Frequency of log summary saves in seconds (60)*
**log-steps**
*Frequency of summary logs in steps (100)*
**model-save-seconds**
*Frequency of model saves (checkpoints) in seconds (600)*
**optimizer**
*Optimizer used to train (rmsprop)
Choices:
adadelta
adagrad
adam
ftrl
momentum
rmsprop
sgd
*
**preprocessing**
*Preprocessing to use (default for model)*
**preprocessors**
*Number of preprocessing threads (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize the
preprocessor thread count for the system.
*
**readers**
*Number of parallel data readers (calculated)
This value is automatically set to logical CPU count / 2 if `auto-scale`
is 'yes'.
When `auto-scale` is 'no' this value can be set to optimize data reader
performance for the system.
*
**train-steps**
*Number of steps to train (train indefinitely)*
**weight-decay**
*Decay on the model weights (4e-05)*
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