# Semantic Segmentation Hyperparameters 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.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTrainingJob.html](https://docs.amazonaws.cn/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\_pretrained\_model | 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.amazonaws.cn/en_us/sagemaker/latest/dg/segmentation-hyperparameters.html)
Default value: `fcn` | **Data Hyperparameters** | Parameter Name | Description | | --- | --- | | num\_classes | The number of classes to segment.
**Required**
Valid values: 2 ≤ positive integer ≤ 254 | | num\_training\_samples | 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\_size | 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\_size | 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\_stopping | Whether to use early stopping logic during training.
**Optional**
Valid values: `True`, `False`
Default value: `False` | | early\_stopping\_min\_epochs | The minimum number of epochs that must be run.
**Optional**
Valid values: integer
Default value: 5 | | early\_stopping\_patience | 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\_stopping\_tolerance | 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\_rate | The initial learning rate.
**Optional**
Valid values: 0 < float ≤ 1
Default value: 0.001 | | lr\_scheduler | 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.amazonaws.cn/en_us/sagemaker/latest/dg/segmentation-hyperparameters.html)
Default value: `poly` | | lr\_scheduler\_factor | 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\_scheduler\_step | 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\_batch\_size | 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.amazonaws.cn/en_us/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\_mini\_batch\_size | 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.amazonaws.cn/en_us/sagemaker/latest/dg/segmentation-hyperparameters.html)
**Optional**
Valid values: positive integer
Default value: 16 | | weight\_decay | 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 |