# Semantic Segmentation Hyperparameters
<a name="segmentation-hyperparameters"></a>

The following tables list the hyperparameters supported by the Amazon SageMaker AI semantic segmentation algorithm for network architecture, data inputs, and training. You specify Semantic Segmentation for training in the `AlgorithmName` of the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) request.

**Network Architecture Hyperparameters**


| Parameter Name | Description | 
| --- | --- | 
| backbone |  The backbone to use for the algorithm's encoder component. **Optional** Valid values: `resnet-50`, `resnet-101`  Default value: `resnet-50`  | 
| use\$1pretrained\$1model |  Whether a pretrained model is to be used for the backbone. **Optional** Valid values: `True`, `False` Default value: `True`  | 
| algorithm |  The algorithm to use for semantic segmentation.  **Optional** Valid values: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/segmentation-hyperparameters.html) Default value: `fcn`  | 

**Data Hyperparameters**


| Parameter Name | Description | 
| --- | --- | 
| num\$1classes |  The number of classes to segment. **Required** Valid values: 2 ≤ positive integer ≤ 254  | 
| num\$1training\$1samples |  The number of samples in the training data. The algorithm uses this value to set up the learning rate scheduler. **Required** Valid values: positive integer  | 
| base\$1size |  Defines how images are rescaled before cropping. Images are rescaled such that the long size length is set to `base_size` multiplied by a random number from 0.5 to 2.0, and the short size is computed to preserve the aspect ratio. **Optional** Valid values: positive integer > 16 Default value: 520  | 
| crop\$1size |  The image size for input during training. We randomly rescale the input image based on `base_size`, and then take a random square crop with side length equal to `crop_size`. The `crop_size` will be automatically rounded up to multiples of 8. **Optional** Valid values: positive integer > 16 Default value: 240  | 

**Training Hyperparameters**


| Parameter Name | Description | 
| --- | --- | 
| early\$1stopping |  Whether to use early stopping logic during training. **Optional** Valid values: `True`, `False` Default value: `False`  | 
| early\$1stopping\$1min\$1epochs |  The minimum number of epochs that must be run. **Optional** Valid values: integer Default value: 5  | 
| early\$1stopping\$1patience |  The number of epochs that meet the tolerance for lower performance before the algorithm enforces an early stop. **Optional** Valid values: integer Default value: 4  | 
| early\$1stopping\$1tolerance |  If the relative improvement of the score of the training job, the mIOU, is smaller than this value, early stopping considers the epoch as not improved. This is used only when `early_stopping` = `True`. **Optional** Valid values: 0 ≤ float ≤ 1 Default value: 0.0  | 
| epochs |  The number of epochs with which to train. **Optional** Valid values: positive integer Default value: 10  | 
| gamma1 |  The decay factor for the moving average of the squared gradient for `rmsprop`. Used only for `rmsprop`. **Optional** Valid values: 0 ≤ float ≤ 1 Default value: 0.9  | 
| gamma2 |  The momentum factor for `rmsprop`. **Optional** Valid values: 0 ≤ float ≤ 1 Default value: 0.9  | 
| learning\$1rate |  The initial learning rate.  **Optional** Valid values: 0 < float ≤ 1 Default value: 0.001  | 
| lr\$1scheduler |  The shape of the learning rate schedule that controls its decrease over time. **Optional** Valid values:  [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/segmentation-hyperparameters.html) Default value: `poly`  | 
| lr\$1scheduler\$1factor |  If `lr_scheduler` is set to `step`, the ratio by which to reduce (multipy) the `learning_rate` after each of the epochs specified by the `lr_scheduler_step`. Otherwise, ignored. **Optional** Valid values: 0 ≤ float ≤ 1 Default value: 0.1  | 
| lr\$1scheduler\$1step |  A comma delimited list of the epochs after which the `learning_rate` is reduced (multiplied) by an `lr_scheduler_factor`. For example, if the value is set to `"10, 20"`, then the `learning-rate` is reduced by `lr_scheduler_factor` after the 10th epoch and again by this factor after 20th epoch. **Conditionally Required** if `lr_scheduler` is set to `step`. Otherwise, ignored. Valid values: string Default value: (No default, as the value is required when used.)  | 
| mini\$1batch\$1size |  The batch size for training. Using a large `mini_batch_size` usually results in faster training, but it might cause you to run out of memory. Memory usage is affected by the values of the `mini_batch_size` and `image_shape` parameters, and the backbone architecture. **Optional** Valid values: positive integer  Default value: 16  | 
| momentum |  The momentum for the `sgd` optimizer. When you use other optimizers, the semantic segmentation algorithm ignores this parameter. **Optional** Valid values: 0 < float ≤ 1 Default value: 0.9  | 
| optimizer |  The type of optimizer. For more information about an optimizer, choose the appropriate link: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/segmentation-hyperparameters.html) **Optional** Valid values: `adam`, `adagrad`, `nag`, `rmsprop`, `sgd`  Default value: `sgd`  | 
| syncbn |  If set to `True`, the batch normalization mean and variance are computed over all the samples processed across the GPUs. **Optional**  Valid values: `True`, `False`  Default value: `False`  | 
| validation\$1mini\$1batch\$1size |  The batch size for validation. A large `mini_batch_size` usually results in faster training, but it might cause you to run out of memory. Memory usage is affected by the values of the `mini_batch_size` and `image_shape` parameters, and the backbone architecture.  [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/segmentation-hyperparameters.html) **Optional** Valid values: positive integer Default value: 16  | 
| weight\$1decay |  The weight decay coefficient for the `sgd` optimizer. When you use other optimizers, the algorithm ignores this parameter.  **Optional** Valid values: 0 < float < 1 Default value: 0.0001  |