Core

• We removed deprecated metric classes BinaryAccuracy and CategoricalAccuracy and which are replaced by Accuracy.

• Multilabel option for Accuracy, Precision, Recall metrics.

• Added other metrics:

  • ConfusionMatrix, IoU, mean IoU

• Operations on metrics: p = Precision(average=False)

  • apply PyTorch operators: mean_precision = p.mean()
  • indexing: precision_no_bg = p[1:]

• Improved our docs with more examples.

• Added FAQ section with best practices.

• Bug fixes

Now in Contributions module

• added TensorboardLogger
• added VisdomLogger
• added PolyaxonLogger
• improved ProgressBar
• New regression metrics
  • Median Absolute Error
  • Median Relative Absolute Error
  • Median Absolute Percentage Error
  • Geometric Mean Relative Absolute Error
  • Canberra Metric
  • Fractional Absolute Error
  • Wave Hedges Distance
  • Geometric Mean Absolute Error
• added new parameter scheduling classes and improved parameters:
  • PiecewiseLinear
  • LRScheduler
  • other helper methods
• added custom events support: CustomPeriodicEvent

Notebook examples

• VAE on MNIST
• CNN for text classification

Nighlty builds with pytorch-nightly as dependency

We also provide pip/conda nighlty builds with pytorch-nightly as dependency:

pip install pytorch-ignite-nightly

or

conda install -c pytorch ignite-nightly 

Core

• We removed deprecated metric classes BinaryAccuracy and CategoricalAccuracy and which are replaced by Accuracy.

• Multilabel option for Accuracy, Precision, Recall metrics.

• Added other metrics:

  • ConfusionMatrix, IoU, mean IoU

• Operations on metrics: p = Precision(average=False)

  • apply PyTorch operators: mean_precision = p.mean()
  • indexing: precision_no_bg = p[1:]

• Improved our docs with more examples.

• Added FAQ section with best practices.

• Bug fixes

Now in Contributions module

• added TensorboardLogger
• added VisdomLogger
• added PolyaxonLogger
• improved ProgressBar
• New regression metrics
  • Median Absolute Error
  • Median Relative Absolute Error
  • Median Absolute Percentage Error
  • Geometric Mean Relative Absolute Error
  • Canberra Metric
  • Fractional Absolute Error
  • Wave Hedges Distance
  • Geometric Mean Absolute Error
• added new parameter scheduling classes and improved parameters:
  • PiecewiseLinear
  • LRScheduler
  • other helper methods
• added custom events support: CustomPeriodicEvent

Notebook examples

• VAE on MNIST
• CNN for text classification

Nighlty builds with pytorch-nightly as dependency

We also provide pip/conda nighlty builds with pytorch-nightly as dependency:

pip install pytorch-ignite-nightly

or

conda install -c pytorch ignite-nightly 

• Improve and fix bug with binary accuracy, precision, recall
• Metrics arithmetics
• ParamScheduler to support multiple optimizers/multiple parameter groups

Thanks to all our contributors !

What's new in this release:

• Contrib module with
  • Parameter schedule
  • TQDM ProgressBar
  • ROC/AUC, AP, MaxAE metrics
  • TBPTT Engine
• New handlers:
  • Terminate on Nan
• New metrics:
  • RunningAverage
  • Merged Categorical/Binary -> Accuracy
• Refactor of examples
• New examples:
  • Fast Neural Style
  • RL

Thanks to all our contributors !

Introduced Engine, Handlers and Metrics.

Metrics:

• BinaryAccuracy
• CategoricalAccuracy
• Loss
• Precision
• Recall
• etc

Handlers:

• ModelCheckpoint
• EarlyStopping
• Timer

Features:

• PyTorch 0.4 support

Examples:

• mnist.py
• mnist_with_tensorboardx.py
• mnist_with_visdom.py
• dcgan.py