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Version: v1.3.0

Experiment settings: Image object detection

The settings for an image object detection experiment are listed and described below.

General settings​

Dataset​

It defines the dataset for the experiment.

Problem category​

This setting defines a particular general problem type category, for example, image.

note
  • The selected problem category (for example, image) determines the options in the Problem type setting.
  • The following option is available when defining the settings of an experiment: From experiment.
    • The From experiment option enables you to utilize the settings of an experiment (another experiment).

Experiment​

Defines the experiment H2O Hydrogen Torch references to initialize the experiment settings. H2O Hydrogen Torch initializes the experiment settings with the values from the selected (built) experiment.

Note

This setting is available only if From experiment is selected in the Problem category setting.

Problem type​

Defines the problem type of the experiment, which also defines the settings H2O Hydrogen Torch displays for the experiment.

Note
  • The selected problem category (in the Problem category setting) determines the available problem types.
  • The selected problem type and experience level determine the settings H2O Hydrogen Torch displays for the experiment.

Model type​

It defines the model type for the experiment.

caution

Whisper models: For inference, deployed Whisper models trained in H2O Hydrogen Torch and later deployed to H2O MLOps truncate audios longer than 30 seconds (>30). In that case, you must cut audio longer than 30 seconds. For example, consider the following high-level process to cut down the audio inputs to 30 seconds or less: 

import base64
import librosa
import math
import soundfile as sf

y, sr = librosa.load("output/user/my_audio_file.mp3")
num_blocks = math.ceil(y.shape[0] / sr / 30)

for curr_block in range(1, num_blocks + 1):
    new_y = y[(curr_block - 1) * 30 * sr:curr_block * 30 * sr]
    wav_file = sf.write("output/user/converted_audio.wav", new_y, samplerate=sr, format="WAV")

    string1 = base64.b64encode(open("output/user/converted_audio.wav", "rb").read()).decode()
    ...
Note
  • The selected problem type, experience level, and model type determine the settings H2O Hydrogen Torch displays for the experiment.
  • Not all problem types support the specification of a model type; in that case, the selected problem type and experience level determine the settings H2O Hydrogen Torch displays for the experiment.
Options

Image object detection

  • Efficientdet
    • EfficientDet models are among the most popular models to tackle image object detection. They are using EfficientNet models as a backbone and a weighted bi-directional feature pyramid network (BiFPN) as the feature network.
      note

      EfficientDet is the default model type for image object detection in H2O Hydrogen Torch. To learn more about EfficientDet, see EfficientDet: Scalable and efficient object detection.

  • Faster Rcnn
    • Faster Region-based Convolutional Neural Networks (FasterRCNN) is an advancement of classical Region-based Convolutional Neural Networks (RCNN) architectures, so-called region-based convolutional neural networks. The core idea is to apply selective search to extract regions of interest from an image, where each ROI might represent a bounding box of an object. Each region of interest (ROI) is fed through a neural network to produce output features used to classify the type of object. A FasterRCNN shares full-image convolutional features with the detection network and thus enables nearly cost-free region proposals, significantly improving the training and inference process compared to classical RCNN or Fast RCNN networks.
      note

      The implementation of FasterRCNNs in H2O Hydrogen Torch enables the selection of a pre-trained vision backbone from an extensive selection. To learn more about FasterRCNN, see Faster R-CNN: Towards real-time object detection with Region Proposal Networks.

  • Fcos
    • Both EfficientDet and FasterRCNN are so-called anchor-based object detection models. A fully convolutional one-stage object detector (FCOS) is a fully convolutional one-stage object detector to solve object detection per pixel. Similar to how semantic segmentation models operate. FOCS is anchor box and proposal free.
      note

      The implementation of FCOS in H2O Hydrogen Torch enables the selection of a pre-trained vision backbone from an extensive selection. To learn more about FCOS, see FCOS: Fully Convolutional One-Stage Object Detection.

Speech recognition

  • Wav2vec2
    • Wav2vec2 models in H2O Hydrogen Torch leverage a) the transformer encoder architecture and b) the connectionist temporal classification (CTC) loss to learn and perform speech recognition.
      • Raw audio waveforms are typically converted into spectrograms then featurized by a convolutional neural network (CNN) before being fed to the transformer model.
    • Wav2vec2 models can be characterized as making predictions tightly coupled with input audio due to the use of the CTC loss and vocabularies at the character level.
      note

      To learn more about Wav2vec2, see wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations.

  • Whisper
    • Whisper models in H2O Hydrogen Torch use a) the transformer encoder-decoder architecture and b) a cross-entropy loss to learn and perform speech recognition.
      • Raw audio waveforms are typically converted into spectrograms then featurized by a convolutional neural network (CNN) before being fed to the transformer model.
    • Whisper models are auto-regressive generative models. While pre-trained Whisper models perform very strongly due to the size of their pre-training corpus, their generative nature may cause them to hallucinate (that is, predict speech that was not spoken) or more commonly, not predict speech when it is spoken.
      note

      To learn more about the Whisper architecture, see Robust Speech Recognition via Large-Scale Weak Supervision.

Import config from YAML​

Defines the .yml file that defines the experiment settings.

Note
  • H2O Hydrogen Torch supports a .yml file import and export functionality. You can download the config settings of finished experiments, make changes, and re-upload them when starting a new experiment in any instance of H2O Hydrogen Torch.

Use previous experiment weights​

Defines whether to initialize the model weights with the weights from the experiment specified in the Experiment setting.

note
  • This setting is available only if From experiment is selected in the Problem category setting
  • A model's weights are available for an experiment (model) of the same problem type and backbone.
  • This setting might be useful in case you want to continue training from a built experiment

Experiment name​

It defines the name of the experiment.

Dataset settings​

Train dataframe​

Defines a .csv or .pq file containing a dataframe with training records that H2O Hydrogen Torch uses to train the model.

note
  • The records are combined into mini-batches when training the model.
  • If a validation dataframe is provided, a fold column is not needed in the train dataframe.
  • You can now import datasets for inference only. To do so, when defining the setting for an experiment, set the Train dataframe setting to None while setting the Test dataframe setting to the relevant dataframe (as a result, H2O Hydrogen Torch utilizes the relevant dataset for predictions and not for training).

Data folder​

Defines the location of the folder containing assets (for example, images or audio clips) the model utilizes for training. H2O Hydrogen Torch loads assets from this folder during training.

Validation strategy​

Specifies the validation strategy H2O Hydrogen Torch uses for the experiment.

note

To properly assess the performance of your trained models, it is common practice to evaluate it on separate holdout data that the model has not seen during training. H2O Hydrogen Torch allows you to specify different strategies for this task fitting your needs.

Options

  • All supported problem types
    • K-fold cross validation
      • Splits the data using the provided optional fold column in the train data or performs an automatic 5-fold cross-validation.
    • Grouped k-fold cross validation
      • Allows to specify a group column based on which the data is split into folds.
    • Custom holdout validation
      • Specifies a separate holdout dataframe.
    • Automatic holdout validation
      • Allows to specify a holdout validation sample size that is automatically generated.

Validation dataframe​

Defines a .csv or .pq file containing a dataframe with validation records that H2O Hydrogen Torch uses to evaluate the model during training.

Note
  • To set a Validation dataframe requires the Validation strategy to be set to Custom holdout validation. In this case, H2O Hydrogen Torch fully respects the choice of a separate validation dataframe and does not perform any internal cross-validation. In other words, the model is trained on the full provided train dataframe, and model performance is evaluated on the provided validation dataframe.
  • The validation dataframe should have the same format as the train dataframe but does not require a fold column.

Selected folds​

Defines the selected validation fold(s) in case of cross-validation; a separate model is trained for each value selected. Each model utilizes the corresponding part of the data as a holdout sample to assess performance while the model is fitted to the rest of the records from the training dataframe. As a result, folds estimate how the model performs in general when used to make predictions on data not used during model training.

Note
  • H2O Hydrogen Torch allows running experiments on a single selected fold for faster experimenting and multiple selected folds to gain more trust in the model's generalization and performance capabilities.
  • The Selected folds setting is only be available if Custom holdout validation is not selected as the Validation strategy.

Test dataframe​

Defines a .csv or .pq file containing a dataframe with test records that H2O Hydrogen Torch uses to test the model.

note
  • The test dataframe should have the same format as the train dataframe but does not require a label column.
  • You can now import datasets for inference only. To do so, when defining the setting for an experiment, set the Train dataframe setting to None while setting the Test dataframe setting to the relevant dataframe (as a result, H2O Hydrogen Torch utilizes the relevant dataset for predictions and not for training).

Data folder test​

Defines the location of the folder containing assets (for example, images, texts, or audio clips) H2O Hydrogen Torch utilizes to test the model. H2O Hydrogen Torch loads the assets from this folder when testing the model. This setting is only available if a test dataframe is selected.

Note

The Data folder test setting appears when you specify a test dataframe in the Test dataframe setting.

Unlabeled dataframe​

Defines a separate .csv or .pq file (depending on the problem type) containing a dataframe with unlabeled records that H2O Hydrogen Torch utilizes to generate pseudo labels. H2O Hydrogen Torch first trains the model with the provided labeled data (Train dataframe). Right after, the model predicts pseudo labels for the provided unlabeled dataframe before doing another training run that combines the original labels and pseudo labels.

note
  • Image regression | Image classification | Image object detection
    • The unlabeled dataframe just needs to contain a single image column
  • Text regression | Text classification
    • The unlabeled dataframe just needs to contain a single text column
  • Audio regression | Audio classification | Speech recognition
    • The unlabeled dataframe just needs to contain a single audio column
  • Image regression | Image classification | Image object detection | Audio regression | Audio classification | Speech recognition
    • Assets (images or audios) need to be located in the Data folder (setting)
  • All supported problem types
    • The training time can significantly increase depending on the size of the unlabeled data
tip

As labeling can be expensive, having additional unlabeled data is quite common. Providing this unlabeled data in H2O Hydrogen Torch trains the model semi-supervised, potentially improving the model quality in contrast to only training on labeled data.

Class name column​

Defines the dataset column containing a list of class names that H2O Hydrogen Torch uses for each instance mask.

X min column​

Defines the dataset column containing a list of minimum X positions H2O Hydrogen Torch uses for each bounding box.

Y min column​

Defines the dataset column containing a list of minimum Y positions H2O Hydrogen Torch uses for each bounding box.

X max column​

Defines the dataset column containing a list of maximum X positions H2O Hydrogen Torch uses for each bounding box.

Y max column​

Defines the dataset column containing a list of maximum Y positions H2O Hydrogen Torch uses for each bounding box.

Image column​

Defines the dataframe column storing the names of images that H2O Hydrogen Torch loads from the data folder and data folder test when training and testing the model.

Data sample​

Defines the percentage of the data to use for the experiment. The default percentage is 100% (1).

tip

Changing the default value can significantly increase the training speed. Still, it might lead to a substantially poor accuracy value. Using 100% (1) of the data for final models is highly recommended.

Image settings​

Image width​

Defines the width H2O Hydrogen Torch uses to rescale the images for training and predictions.

note

Depending on the original image size, a bigger width can generate a higher accuracy value.

Image height​

Defines the width H2O Hydrogen Torch uses to rescale the images for training and predictions.

note

Depending on the original image size, a bigger width can generate a higher accuracy value.

Image channels​

Defines the number of channels the train images contain.

note
  • Typically images have three input channels (red, green, and blue (RGB)), but grayscale images have only 1. When you provide image data in a NumPy data format, any number of channels is allowed. For this reason, data scientists can specify the number of channels.
  • The defined number of channels also refers to the provided validation and test datasets.

Image normalization​

Grid search hyperparameter

Defines the transformer to normalize the image data before training the model.

note

Usually, state-of-the-art image models normalize the training images by scaling values of each of the input channels to predefined means and standard deviations.

Options

Image regression | Image classification | Image object detection | Image semantic segmentation | Image instance segmentation | Image metric learning

  • Channel
    • Calculates mean and standard deviation per channel in all the images in the batch and then applies per channel normalization: subtracts mean and divides by standard deviation.
  • Image
    • Calculates mean and standard deviation per image and then applies normalization.
  • ImageNet
    • Divides input images by 255 and normalizes with mean and standard deviation equal to (0.485, 0.456, 0.406) and (0.229, 0.224, 0.225) per channel, respectively.
  • Inception
    • Divides input images by 255 and normalizes with mean and standard deviation equal to 0.5.
  • Min_Max
    • Calculates minimum and maximum values in all the images in the batch and then applies min-max normalization: subtracts min and divides by the max and min difference.
  • No
    • No normalization is applied to the input images.
  • Simple
    • Divides input images by 255.

3D image classification | 3D image regression | 3D image semantic segmentation

  • No
    • No normalization is applied to the input images.
  • Simple
    • Divides input images by 255.
  • Min_Max
    • Calculates minimum and maximum values in all the images in the batch and then applies min-max normalization: subtracts min and divides by the max and min difference.

note

Usually, state-of-the-art image models normalize the training images by scaling values of each of the input channels to predefined means and standard deviations.

Augmentation settings​

Augmentations strategy​

Grid search hyperparameter

Defines the augmentation strategy to apply to the input images. Soft, Medium, and Hard values correspond to the strength of the augmentations to apply.

Options

Image regression | Image classification | Image object detection | Image semantic segmentation | Image instance segmentation | Image metric learning

  • Soft
    • The Soft strategy applies image Resize and random HorizontalFlip during model training while applying image Resize during model inference.
  • Medium
    • The Medium strategy adds ShiftScaleRotate and CoarseDropout to the list of the train augmentations.
  • Hard
    • The Hard strategy applies RandomResizedCrop (instead of Resize) during model training while adding RandomBrightnessContrast to the list of train augmentations.
  • Custom

3D image classification | 3D image regression | 3D image semantic segmentation

  • Soft
    • The Soft strategy applies image Resize and random HorizontalFlip during model training while applying image Resize during model inference.
  • Medium
    • The Medium strategy adds ShiftScaleRotate and CoarseDropout to the list of the train augmentations.
  • Hard
    • The Hard strategy applies RandomResizedCrop (instead of Resize) during model training while adding RandomBrightnessContrast to the list of train augmentations.

note

Augmentations are ways to modify train images while keeping the target values valid, such as flipping the image or adding noise. Distorting training images do not influence the expected prediction of the model but enrich the training data. Augmentations help generalize the model better and improve its accuracy.

Mix image​

Grid search hyperparameter

Defines the image mix augmentation to use during model training.

Options

Image regression | Image classification | Image object detection | Image semantic segmentation | Image instance segmentation

  • Mixup
    • Mixup overlays (mixes) two images one on another based on a random ratio. To learn more about this mix augmentation approach, refer to the following article: mixup: BEYOND EMPIRICAL RISK MINIMIZATION.
      note

      For an image object detection experiment using Mixup, H2O Hydrogen Torch uses the union of all the target boxes in mixed images.

  • Cutmix
    • Cutmix replaces an image region with a patch from another image; the region size is based on a random ratio. To learn more about this mix augmentation approach, refer to the following article: SOLVING LINEAR SYSTEMS OVER TROPICAL SEMIRINGS THROUGH NORMALIZATION METHOD AND ITS APPLICATIONS.
      note

      For an image object detection experiment using Cutmix, H2O Hydrogen Torch uses the target boxes from the corresponding region from each image. Also, with Cutmix selected, H2O Hydrogen Torch cuts out and replaces only the corners of the images with a patch from another image.

  • Disabled
    • No augmentation is applied.

3D image classification | 3D image regression | 3D image semantic segmentation

  • Mixup
    • Mixup overlays (mixes) two images one on another based on a random ratio. To learn more about this mix augmentation approach, refer to the following article: mixup: BEYOND EMPIRICAL RISK MINIMIZATION.
      note

      For an image object detection experiment using Mixup, H2O Hydrogen Torch uses the union of all the target boxes in mixed images.

  • Disabled
    • No augmentation is applied.

Architecture settings​

Pretrained​

Grid search hyperparameter

Defines whether the neural network should start with pre-trained weights. When this setting is On, the training of the neural network starts with a pre-trained model on a generic task. When turned Off, the initial weights of the neural network to train become random.

Backbone​

Grid search hyperparameter

Defines the backbone neural network architecture to train the model.

Note
  • Image regression | Image classification | Image metric learning | Audio regression | Audio classification
    • H2O Hydrogen Torch accepts backbone neural network architectures from the timm library (select or enter the architecture name)
  • Image object detection
    • H2O Hydrogen Torch provides several backbone state-of-the-art neural network architectures for model training. When you select Faster RCnn or Fcos as the model type for the experiment, you can input any architecture name from the timm library. When you select Efficientdet as the model type for the experiment, you can input any architecture name from the efficientdet-pytorch library
  • Image semantic segmentation | Image instance segmentation
    • H2O Hydrogen Torch accepts backbone neural network architectures from the segmentation-models-pytorch library (select or enter the architecture name).
  • 3D image regression | 3D image classification
    • H2O Hydrogen Torch accepts backbone (encoder) neural network architectures from a subset (resnet and efficientnet) of the timm library (select or enter the architecture name).
  • Text regression | Text classification | Text token classification | Text span prediction | Text sequence to sequence | Text metric learning
    • H2O Hydrogen Torch accepts backbone neural network architectures from the Hugging Face library (select or enter the architecture name)
  • Speech recognition
    • HuggingFace Wav2Vec2 CTC models are supported
tip
  • All problem types
    • Usually, it is good to use simpler architectures for quicker experiments and larger models when aiming for the highest accuracy
  • Speech recognition
    • If possible, leverage backbones pre-trained closely to your use case (for example, noisy audio, casual speech, etc.)

Drop path rate​

Grid search hyperparameter

Defines the drop path rate for the Backbone to use during training. The drop path rate prevents co-adaptation of parallel paths in networks, similar to how dropout prevents co-adaption of activations. If set to Default, it picks the default setting for the respective backbone.

note

This setting is available when Efficientdet is selected as the model type for the experiment.

Anchor num scales​

Grid search hyperparameter

Defines the number of anchor scales to use for each anchor box. You may want to change this to work with more fine-grained scales. Note that changing this setting resets the head of the pre-trained model; in most use cases, it is recommended to use the default value.

note

This setting is available when Efficientdet is selected as the model type for the experiment.

Anchor scale​

Grid search hyperparameter

Defines the general scale factor for all anchor boxes; you may want to change this if your dataset contains a large amount of particularly small or large boxes.

note

This setting is available when Efficientdet is selected as the model type for the experiment.

Anchor aspect ratios​

Defines the different anchor aspect ratios for anchor boxes; in the best case, the selected anchor aspect ratios should match the default shapes in the dataset. Note that changing this setting resets the head of the pre-trained model: in most use cases, it is recommended to use the default value.

note

This setting is available when Efficientdet is selected as the model type for the experiment.

Anchor Iou match threshold​

Grid search hyperparameter

Defines the IoU threshold for matching anchor boxes. In particular, the IoU threshold is used to determine whether an anchor box matches a ground truth box.

example

If you set the Anchor IoU match threshold to 0.5, the anchor box only matches a ground truth box if the IoU is greater than 50%.

In other words, the IoU threshold determines positive labels for anchors.

note

This setting is available when Efficientdet is selected as the model type for the experiment.

Training settings​

Optimizer​

Grid search hyperparameter

Defines the algorithm or method (optimizer) to use for model training. The selected algorithm or method defines how the model should change the attributes of the neural network, such as weights and learning rate. Optimizers solve optimization problems and make more accurate updates to attributes to reduce learning losses.

Options

Learning rate​

Grid search hyperparameter

Defines the learning rate H2O Hydrogen Torch uses when training the model, specifically when updating the neural network's weights. The learning rate is the speed at which the model updates its weights after processing each mini-batch of data.

note
  • Learning rate is an important setting to tune as it balances under- and overfitting.
  • The number of epochs highly impacts the optimal value of the learning rate.

Differential learning rate layers​

Defines the learning rate to apply to certain layers of a model. H2O Hydrogen Torch applies the regular learning rate to layers without a specified learning rate.

Options

Image regression | Image classification | Text regression | Text classification | Text token classification | Audio regression | Audio classification

  • Backbone
    • H2O Hydrogen Torch applies a different learning rate to a body of the neural network architecture.
  • Head
    • H2O Hydrogen Torch applies a different learning rate to a head of the neural network architecture.

Image object detection

The options for an image object detection experiment are different based on the selected Model type (setting). Options:

  • If you select EfficientDet as the experiment's Model type (setting), the following options are available:

    Options

    • Backbone
      • H2O Hydrogen Torch applies a different learning rate to a body of the EfficientDet architecture.
    • FPN
      • H2O Hydrogen Torch applies a different learning rate to a Feature Pyramid Network (FPN) block of the EfficientDet architecture.
    • class_net
      • H2O Hydrogen Torch applies a different learning rate to a classification head of the EfficientDet architecture.
    • box_net
      • H2O Hydrogen Torch applies a different learning rate to a box regression head of the EfficientDet architecture.

  • If you select Faster R-CNN as the experiment's Model type (setting), the following options are available:

    Options

    • Body
      • H2O Hydrogen Torch applies a different learning rate to a body of the Faster R-CNN architecture.
    • FPN
      • H2O Hydrogen Torch applies a different learning rate to a Feature Pyramid Network (FPN) block in the Faster R-CNN architecture.
    • RPN
      • H2O Hydrogen Torch applies a different learning rate to a Region Proposal block of the Faster R-CNN architecture.
    • ROI heads
      • H2O Hydrogen Torch applies a different learning rate to the Faster R-CNN architecture proposal heads.

  • If you select FCOS as the experiment's Model type (setting), the following options are available:

    Options

    • Body
      • H2O Hydrogen Torch applies a different learning rate to a body of the FCOS architecture.
    • FPN
      • H2O Hydrogen Torch applies a different learning rate to a Feature Pyramid Network (FPN) block of the FCOS architecture.
    • classification_head
      • H2O Hydrogen Torch applies a different learning rate to the classification head of the FCOS architecture.
    • regression_head
      • H2O Hydrogen Torch applies a different learning rate to a box regression head of the FCOS architecture.

Image semantic segmentation

  • Encoder
    • H2O Hydrogen Torch applies a different learning rate to the encoder of the neural network architecture.
  • Decoder
    • H2O Hydrogen Torch applies a different learning rate to the decoder of the neural network architecture.
  • Segmentation head
    • H2O Hydrogen Torch applies a different learning rate to the head of the neural network architecture.

3D image semantic segmentation | Text sequence to sequence

  • Encoder
    • H2O Hydrogen Torch applies a different learning rate to the encoder of the neural network architecture.
  • Decoder
    • H2O Hydrogen Torch applies a different learning rate to the decoder of the neural network architecture.

Image instance segmentation

  • Encoder
    • H2O Hydrogen Torch applies a different learning rate to the encoder of the neural network architecture.
  • Decoder
    • H2O Hydrogen Torch applies a different learning rate to the decoder of the neural network architecture.
  • Segmentation head
    • H2O Hydrogen Torch applies a different learning rate to the head of the neural network architecture.

Image metric learning | Text metric learning

  • Backbone
    • H2O Hydrogen Torch applies a different learning rate to a body of the neural network architecture.
  • Neck
    • H2O Hydrogen Torch applies a different learning rate to a neck of the neural network architecture.
  • Loss
    • H2O Hydrogen Torch applies a different learning rate to an ArcFace block of the neural network architecture.

Text regression

  • Backbone
    • H2O Hydrogen Torch applies a different learning rate to a body of the neural network architecture.

Text span prediction

  • qa_outputs

tip

A common strategy is to apply a lower learning rate to the backbone of a model for better convergence and training stability.

note

Different layers are available for different problem types.

Batch size​

Grid search hyperparameter

Defines the number of training examples a mini-batch uses during an iteration of the training model to estimate the error gradient before updating the model weights. Batch size defines the batch size used per a single GPU.

note

During model training, the training data is packed into mini-batches of a fixed size.

Automatically adjust batch size​

If this setting is turned On, H2O Hydrogen Torch checks whether the Batch size specified fits into the GPU memory. If a GPU out-of-memory (OOM) error occurs, H2O Hydrogen Torch automatically decreases the Batch size by a factor of 2 units until it fits into the GPU memory or Batch size equals 1.

Drop last batch​

H2O Hydrogen Torch drops the last incomplete batch during model training when this setting is turned On.

note

H2O Hydrogen Torch groups the train data into mini-batches of equal size during the training process, but the last batch can have fewer records than the others. Not dropping the last batch can lead to a less robust gradient estimation while causing a more volatile training step.

Epochs​

Grid search hyperparameter

Defines the number of epochs to train the model. In other words, it specifies the number of times the learning algorithm goes through the entire training dataset.

note
  • The Epochs setting is an important setting to tune because it balances under- and overfitting.
  • The learning rate highly impacts the optimal value of the epochs.
  • For the following supported problem types, H2O Hydrogen Torch now enables you to utilize/deploy a pre-trained model trained on zero epochs (where H2O Hydrogen Torch does not train the model and the pretrained model (experiment) can be deployed as-is):
    • Speech recognition
    • Text sequence to sequence
    • text span prediction

Schedule​

Grid search hyperparameter

Defines the learning rate schedule H2O Hydrogen Torch utilizes during model training. Specifying a learning rate schedule prevents the learning rate from staying the same. Instead, a learning rate schedule causes the learning rate to change over iterations, typically decreasing the learning rate to achieve a better model performance and training convergence.

Options

  • All supported problem types
    • Constant
      • H2O Hydrogen Torch applies a constant learning rate during the training process.
    • Cosine
      • H2O Hydrogen Torch applies a cosine learning rate that follows the values of the cosine function.
    • Linear
      • H2O Hydrogen Torch applies a linear learning rate that decreases the learning rate linearly.

Warmup epochs​

Grid search hyperparameter

Defines the number of epochs to warm up the learning rate where the learning rate should increase linearly from 0 to the desired learning rate.

Weight decay​

Grid search hyperparameter

Defines the weight decay that H2O Hydrogen Torch uses for the optimizer during model training.

note

Weight decay is a regularization technique that adds an L2 norm of all model weights to the loss function while increasing the probability of improving the model generalization.

Gradient clip​

Grid search hyperparameter

Defines the maximum norm of the gradients H2O Hydrogen Torch specifies during model training. Defaults to 0, no clipping. When a value greater than 0 is specified, H2O Hydrogen Torch modifies the gradients during model training. H2O Hydrogen Torch uses the specified value as an upper limit for the norm of the gradients, calculated using the Euclidean norm over all gradients per batch.

note

This setting can help model convergence when extreme gradient values cause high volatility of weight updates.

Grad accumulation​

Grid search hyperparameter

Defines the number of gradient accumulations before H2O Hydrogen Torch updates the neural network weights during model training.

note
  • Grad accumulation can be beneficial if only small batches are selected for training. With gradient accumulation, the loss and gradients are calculated after each batch, but it waits for the selected accumulations before updating the model weights. You can control the batch size through the Batch size setting.
  • Changing the default value of Grad Accumulation might require adjusting the learning rate and batch size.

Save best checkpoint​

Determines if H2O Hydrogen Torch should save the model weights of the epoch exhibiting the best validation metric. When turned On, H2O Hydrogen Torch saves the model weights for the epoch exhibiting the best validation metric. When turned Off, H2O Hydrogen Torch saves the model weights after the last epoch is executed.

note
  • This setting should be turned On with care as it has the potential to lead to overfitting of the validation data.
  • The default goal should be to attempt to tune models so that the last or very last epoch is the best epoch.
  • Suppose an evident decline for later epochs is observed in logging. In that case, it is usually better to adjust hyperparameters, such as reducing the number of epochs or increasing regularization, instead of turning this setting On.

Evaluation epochs​

Defines the number of epochs H2O Hydrogen Torch uses before each validation loop for model training. In other words, it determines the frequency (in a number of epochs) to run the model evaluation on the validation data.

note
  • Increasing the number of Evaluation Epochs can speed up an experiment.
  • The Evaluation epochs setting is available only if the following setting is turned Off: Save Best Checkpoint.

Evaluate before training​

Determines whether to perform a validation run before training. This setting is potentially helpful for assessing the performance of zero-shot pertained backbones and checking the modeling pipeline.

note

The following supported problem types support externally pretrained zero-shot models (while problem types that do not contain this support fit a new head on top of a backbone):

  • Text span prediction
  • Text sequence to sequence
  • Speech recognition

Calculate train metric​

Determines whether the model metric should also be calculated for the training data at the end of the training. When On, the model metric is calculated for the training data. The resulting values do not indicate the true model performance because they are based on H2O Hydrogen Torch's identical data records for model training but can give insights into over/underfitting.

Train validation data​

Defines whether the model should use the entire train and validation dataset during model training. When turned On, H2O Hydrogen Torch uses the whole train dataset and validation data to train the model.

note
  • H2O Hydrogen Torch also evaluates the model on the provided validation fold. Validation is always only on the provided validation fold.
  • H2O Hydrogen Torch uses both datasets for model training if you provide a train and validation dataset.
    • To define a training dataset, use the Train dataframe setting. For more information, see Train dataframe.
    • To define a validation dataset, use the Validation dataframe setting. For more information, see Validation dataframe.
  • The Train validation data setting is only available if you turned the Save best checkpoint setting Off.
  • Turning On the Train validation data setting should produce a model that you can expect to perform better because H2O Hydrogen Torch trained the model on more data. Thought, also note that using the entire train dataset and out-of-fold validation dataset generally causes the model's accuracy to be overstated as information from the validation data is incorporated into the model during the training process.
    note

    If you have five folds and set fold 0 as validation, H2O Hydrogen Torch usually trains on folds 1-4 and reports on fold 0. With Train validation data turned On, we can add fold 0 to the training, but H2O Hydrogen Torch still reports its accuracy. As a result, it overstated for fold 0 but should be better for any unseen (test) data/production scenarios. For that reason, you usually want to consider this setting after running your experiments and deciding on models.

Build scoring pipelines​

Determines whether the experiment (model) automatically generates an H2O MLOps pipeline and Python scoring pipeline at the end of the experiment. If turned Off, you can still create scoring pipelines on demand when the experiment is complete (e.g., when you click Download soring or Download MLOps).

Box loss weight​

Defines the weight of the box loss in EfficientDet (a type of object detection model); it is used to balance the loss of the bounding box regression and classification.

note

This setting is available when Efficientdet is selected as the model type for the experiment.

Focal Cls loss alpha​

Defines the alpha hyperparameter value in the focal class loss function; for more information, refer to the following paper: Focal Loss for Dense Object Detection.

note

This setting is available when Efficientdet is selected as the model type for the experiment.

Focal Cls loss gamma​

Defines the gamma hyperparameter value in the focal class loss function; for more information, refer to the following paper: Focal Loss for Dense Object Detection.

note

This setting is available when Efficientdet is selected as the model type for the experiment.

Prediction settings​

Metric​

Defines the metric to evaluate the model's performance.

Options

Image regression | 3D image regression | Text regression | Audio regression

  • MAE: Mean absolute error
    • The Mean Absolute Error (MAE) is an average of the absolute errors. The MAE units are the same as the predicted target, which is useful for understanding whether the size of the error is of concern or not. The smaller the MAE the better the model’s performance.
  • MSE: Mean squared error
    • The MSE metric measures the average of the squares of the errors or deviations. MSE takes the distances from the points to the regression line (these distances are the “errors”) and squaring them to remove any negative signs. MSE incorporates both the variance and the bias of the predictor.
    • MSE also gives more weight to larger differences. The bigger the error, the more it is penalized. For example, if your correct answers are 2,3,4 and the algorithm guesses 1,4,3, then the absolute error on each one is exactly 1, so squared error is also 1, and the MSE is 1. But if the algorithm guesses 2,3,6, then the errors are 0,0,2, the squared errors are 0,0,4, and the MSE is a higher 1.333. The smaller the MSE, the better the model’s performance.
  • RMSE: Root mean squared error
    • The Root Mean Sqaured Error (RMSE) metric evaluates how well a model can predict a continuous value. The RMSE units are the same as the predicted target, which is useful for understanding if the size of the error is of concern or not. The smaller the RMSE, the better the model’s performance.
    • RMSE penalizes outliers more, as compared to MAE, so it is useful if we want to avoid having large errors.
  • MAPE: Mean absolute percentage error
    • Mean Absolute Percentage Error (MAPE) measures the size of the error in percentage terms. It is calculated as the average of the unsigned percentage error.
    • MAPE is useful when target values are across different scales.
  • SMAPE Symmetric mean absolute percentage error
    • Unlike the MAPE, which divides the absolute errors by the absolute actual values, the SMAPE divides by the mean of the absolute actual and the absolute predicted values. This is important when the actual values can be 0 or near 0. Actual values near 0 cause the MAPE value to become infinitely high. Because SMAPE includes both the actual and the predicted values, the SMAPE value can never be greater than 200%.
  • R2: R squared
    • The R2 value represents the degree that the predicted value and the actual value move in unison. The R2 value varies between 0 and 1 where 0 represents no correlation between the predicted and actual value and 1 represents complete correlation.

Image classification | 3D image classification | Text classification | Audio classification

  • LogLoss: Logarithmic loss
    • The logarithmic loss metric can be used to evaluate the performance of a binomial or multinomial classifier. Unlike AUC which looks at how well a model can classify a binary target, logloss evaluates how close a model’s predicted values (uncalibrated probability estimates) are to the actual target value. For example, does a model tend to assign a high predicted value like .80 for the positive class, or does it show a poor ability to recognize the positive class and assign a lower predicted value like .50? Logloss can be any value greater than or equal to 0, with 0 meaning that the model correctly assigns a probability of 0% or 100%.
  • ROC_AUC: Area under the receiver operating characteristic curve
    • This model metric is used to evaluate how well a binary classification model is able to distinguish between true positives and false positives. For multi-class problems, this score is computed by micro-averaging the ROC curves for each class.
    • An Area Under the Curve (AUC) of 1 indicates a perfect classifier, while an AUC of .5 indicates a poor classifier whose performance is no better than random guessing.
  • F1
    • The F1 score is calculated from the harmonic mean of the precision and recall. An F1 score of 1 means both precision and recall are perfect, and the model correctly identified all the positive cases and didn’t mark a negative case as a positive case. If either precision or recall is very low, it is reflected with an F1 score closer to 0.
    • Formula: F1 = 2 (Precision * Recall / Precision + Recall)
      • Precision is the positive observations (true positives) the model correctly identified from all the observations it labeled as positive (the true positives + the false positives).
      • Recall is the positive observations (true positives) the model correctly identified from all the actual positive cases (the true positives + the false negatives).
    • Micro-averaging: H2O Hydrogen Torch micro-averages the F1 metric (score).
      • Multi-class: For multi-class classification experiments utilizing an F1 metric, the derived micro-average F1 metric might look suspicious; in that case, the micro-average F1 metric is numerically equivalent to the accuracy score.
      • Binary: For binary classification experiments utilizing an F1 metric, the label column needs to contain 0/1 values. If the column contains string values, the column is transformed into multiple columns using a one-hot encoder method resulting in the experiment being treated as a multi-class classification experiment while leading to an incorrect calculation of the F1 metric.
  • F2
    • The F2 score is the weighted harmonic mean of the precision and recall (given a threshold value). Unlike the F1 score, which gives equal weight to precision and recall, the F2 score gives more weight to recall than to precision. More weight should be given to recall for cases where False Negatives are considered worse than False Positives. For example, if your use case is to predict which customers will churn, you may consider False Negatives worse than False Positives. In this case, you want your predictions to capture all of the customers that will churn. Some of these customers may not be at risk for churning, but the extra attention they receive is not harmful. More importantly, no customers actually at risk of churning have been missed.
    • Formula: F2 = 5 (Precision * Recall / (4 * Precision) + Recall)
      • Precision is the positive observations (true positives) the model correctly identified from all the observations it labeled as positive (the true positives + the false positives).
      • Recall is the positive observations (true positives) the model correctly identified from all the actual positive cases (the true positives + the false negatives).
    • Micro-averaging: H2O Hydrogen Torch micro-averages the F2 metric (score).
      • Multi-class: For multi-class classification experiments utilizing an F2 metric, the derived micro-average F2 metric might look suspicious; in that case, the micro-average F2 metric is numerically equivalent to the accuracy score.
      • Binary: For binary classification experiments utilizing an F2 metric, the label column needs to contain 0/1 values. If the column contains string values, the column is transformed into multiple columns using a one-hot encoder method resulting in the experiment being treated as a multi-class classification experiment while leading to an incorrect calculation of the F2 metric.
  • F05
    • The F05 score is the weighted harmonic mean of the precision and recall (given a threshold value). Unlike the F1 score, which gives equal weight to precision and recall, the F05 score gives more weight to precision than to recall. More weight should be given to precision for cases where False Positives are considered worse than False Negatives. For example, if your use case is to predict which products you will run out of, you may consider False Positives worse than False Negatives. In this case, you want your predictions to be very precise and only capture the products that will definitely run out. If you predict a product will need to be restocked when it actually doesn’t, you incur cost by having purchased more inventory than you actually need.
    • Formula: F05 = 1.25 (Precision * Recall / (0.25 * Precision) + Recall)
      • Precision is the positive observations (true positives) the model correctly identified from all the observations it labeled as positive (the true positives + the false positives).
      • Recall is the positive observations (true positives) the model correctly identified from all the actual positive cases (the true positives + the false negatives).
    • Micro-averaging: H2O Hydrogen Torch micro-averages the F05 metric (score).
      • Multi-class: For multi-class classification experiments utilizing an F05 metric, the derived micro-average F05 metric might look suspicious; in that case, the micro-average F05 metric is numerically equivalent to the accuracy score.
      • Binary: For binary classification experiments utilizing an F05 metric, the label column needs to contain 0/1 values. If the column contains string values, the column is transformed into multiple columns using a one-hot encoder method resulting in the experiment being treated as a multi-class classification experiment while leading to an incorrect calculation of the F05 metric.
  • Precision
    • The precision metric measures the ratio of correct true positives among all predicted positives.
    • Formula: Precision = True Positive / (True Positive + False Positive)
    • Micro-averaging: H2O Hydrogen Torch micro-averages the precision metric (score).
      • Multi-class: For multi-class classification experiments utilizing a precision metric, the derived micro-average precision metric might look suspicious; in that case, the micro-average precision metric is numerically equivalent to the accuracy score.
      • Binary: For binary classification experiments utilizing a precision metric, the label column needs to contain 0/1 values. If the column contains string values, the column is transformed into multiple columns using a one-hot encoder method resulting in the experiment being treated as a multi-class classification experiment while leading to an incorrect calculation of the precision metric.
  • Recall
    • The recall metric measures the ratio of true positives predicted correctly.
    • Formula: Recall = True Positive / (True Positive + False Negative)
    • Micro-averaging: H2O Hydrogen Torch micro-averages the recall metric (score).
      • Multi-class: For multi-class classification experiments utilizing a recall metric, the derived micro-average recall metric might look suspicious; in that case, the micro-average recall metric is numerically equivalent to the accuracy score.
      • Binary: For binary classification experiments utilizing a recall metric, the label column needs to contain 0/1 values. If the column contains string values, the column is transformed into multiple columns using a one-hot encoder method resulting in the experiment being treated as a multi-class classification experiment while leading to an incorrect calculation of the recall metric.
  • Accuracy
    • In binary classification, Accuracy is the number of correct predictions made as a ratio of all predictions made. In multiclass classification, the set of labels predicted for a sample must exactly match the corresponding set of labels in target values.
  • MCC: Matthews correlation coefficient
    • The goal of the Matthews Correlation Coefficient (MCC) metric is to represent the confusion matrix of a model as a single number. The MCC metric combines the true positives, false positives, true negatives, and false negatives using the following MCC equation: 𝑀𝐶𝐶=𝑇𝑃𝑥𝑇𝑁−𝐹𝑃𝑥𝐹𝑁/√(𝑇𝑃+𝐹𝑃)(𝑇𝑃+𝐹𝑁)(𝑇𝑁+𝐹𝑃)(𝑇𝑁+𝐹𝑁).
    • Unlike metrics like Accuracy, MCC is a good scorer to use when the target variable is imbalanced. In the case of imbalanced data, high Accuracy can be found by predicting the majority class. Metrics like Accuracy and F1 can be misleading, especially in the case of imbalanced data, because they do not consider the relative size of the four confusion matrix categories. MCC, on the other hand, takes the proportion of each class into account. The MCC value ranges from -1 to 1 where -1 indicates a classifier that predicts the opposite class from the actual value, 0 means the classifier does no better than random guessing, and 1 indicates a perfect classifier.

Image object detection

  • mAP: Mean average precision

Image semantic segmentation | 3D image semantic segmentation

  • IoU: Intersection over union
  • Dice

Image instance segmentation

  • COCO_mAP: COCO (Common Objects in Context) mean average precision

Image metric learning | Text metric learning

  • mAP: Mean average precision

Text token classification

  • CONLL_MICRO_F1_SCORE
    • Macro F1 score calculated in CoNLL style
  • CONLL_MACRO_F1_SCORE
    • Micro F1 score calculated in CoNLL style
  • MICRO_F1_SCORE: Micro F1 score
  • MACRO_F1_SCORE: Macro F1 score

Text span prediction

  • Jaccard
  • F1
  • Accuracy
  • Top_2_Accuracy
  • Top_3_Accuracy
  • Top_4_Accuracy
  • Top_5_Accuracy

Text sequence to sequence

  • BLEU
    • Computes the BLEU metric given hypotheses and references
  • CHRF
    • Computes the chrF(++) metric given hypotheses and references
  • TER
    • Computes the translation edit rate metric given hypotheses and references

Speech recognition

  • WER: Word error rate
  • CER: Character error rate

Metric Iou threshold​

Defines the Intersection Over Union (IoU) threshold to calculate the selected metric for image object detection.

note

When calculating metrics, predicted bounding boxes with an IoU (with the true boxes) above the specified IoU threshold are treated as true positives.

Nms Iou threshold​

Defines the Intersection Over Union (IoU) threshold when calculating post-processing non-maximum suppression (NMS).

note

Non-maximum suppression (NMS) is a post-processing step that reduces the number of bounding boxes predicted by the model. The NMS algorithm removes overlap boxes based on the selected IoU threshold. NMS keeps the higher scoring box.

Max det per image​

Defines the maximum number of detections per image that the model returns.

Probability threshold​

  • Image instance segmentation | Image semantic segmentation | 3D image semantic segmentation
    • Defines the probability threshold; a predicted pixel is treated as positive if its probability is larger than the probability threshold.
  • Image object detection
    • Defines the probability threshold that the model utilizes to identify predicted bounding boxes with confidence larger than the defined probability threshold. Predicted bounding boxes above the defined probability threshold are added to the validation and test .csv files in the downloaded model predictions .zip file.
  • Audio classification | Image classification | 3D image classification | Text classification
    • Defines a threshold for threshold-dependent classification metrics (e.g. F1). For multi-class classification, argmax is used.
      note

      The defined threshold is used as a default threshold when displaying all other threshold-dependent metrics.

Environment settings​

GPUs​

Determines the list of GPUs H2O Hydrogen Torch can use for the experiment. GPUs are listed by name, referring to their system ID (starting from 1). If no GPUs selected, CPU is used for model training.

Number of seeds per run​

Defines the number of seeds to use for a single run. If more than one seed is selected, each experiment runs multiple times.

note
  • Deep learning models can sometimes exhibit certain randomness in individual runs. Running an experiment multiple times with multiple seeds, can give insights into stability of results.
  • In case of high randomness, better judgement can be made about the performance of a model with certain hyperparameter settings, by comparing the average results across seeds, for example in a grid search scenario.

Number of GPUs per run​

Defines the number of GPUs to use for a single run when training the model. A single run might represent a single fold, a single seed run or a single grid search run.

example

If 5 GPUs are available, it is possible to run a 5-fold cross-validation in parallel using a single GPU per fold.

note
  • The available GPUs are the ones that can be enabled using the GPUs setting.
  • If the number of GPUs is less than or equal to 1, this setting (Number of GPUs per run ) is not available.

Mixed precision training​

Determines whether to use mixed-precision during model training. When turned Off, H2O Hydrogen Torch does not use mixed-precision for training.

Note

Mixed-precision is a technique that helps decrease memory consumption and increases training speed.

Mixed precision inference​

Determines whether to use mixed-precision during model inference.

note

Mixed-precision is a technique that helps decrease memory consumption and increases inference speed.

Sync batch normalization​

Determines whether to synchronize batch normalization across GPUs in a distributed data-parallel (DDP) mode. In other words, when turned On, multi-GPU training is enabled to synchronize the batch normalization layers of the model across GPUs. In a nutshell, H2O Hydrogen Torch with multi GPU splits the batch across GPUs, and therefore, when a normalization layer wants to normalize data, it has access only to the part of the batch stored on the device. As a result, it works out of the box but gives better results if the data in all GPUs are collected to normalize the data of the entire batch.

Note

When turned On, data scientists can expect the training speed to drop slightly while the model's accuracy improves. However, this rarely happens in practice and only occurs under specific problem types and defined batch sizes.

Number of workers​

Defines the number of workers H2O Hydrogen Torch uses for the DataLoader. In other words, it defines the number of CPU processes to use when reading and loading data to GPUs during model training.

Seed​

Defines the random seed value that H2O Hydrogen Torch uses during model training. It defaults to -1, an arbitrary value. When the value is modified (not -1), the random seed allows results to be reproducible—defining a seed aids in obtaining predictable and repeatable results every time. Otherwise, not modifying the default seed value (-1) leads to random numbers at every invocation.

Logging settings​

Logger​

Defines the logger type that H2O Hydrogen Torch uses for model training

Options

  • All supported problem types
    • None
      • H2O Hydrogen Torch does not use any logger.
    • Neptune

Neptune API token​

Defines the Neptune API token to validate all subsequent Neptune API calls.

Neptune project​

Defines the Neptune project to access if you selected Neptune in the Logger setting.

Log grad norm​

Determines whether to log the total grad norm before and after clipping.

note

This setting adds a small overhead during the experiment runtime but can help determine if the gradients are exploding or unstable.

tip

Turn this setting on if you suspect unstable gradients; as a result, you may then choose a value for the gradient clip to prevent exploding gradients.

Number of images​

This setting defines the number of images to show in the experiment Insights tab.

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