Highlights

GluonCV 0.10.0 release features a new Auto Module designed to bootstrap training tasks with less code and effort:

• simpler and better custom dataset loading experience with pandas DataFrame visualization. Comparing with obsolete code based dataset composition, it allows you to load arbitrary datasets faster and more reliable.

• one liner fit function with configuration file support(yaml configuration file)

• built-in HPO support, for effortless tuning of hyper-parameters

gluoncv.auto

This release includes a new module called gluoncv.auto, with gluoncv.auto you can access many high-level APIs such as data, estimators and tasks.

gluoncv.auto.data

auto.data module is designed to load arbitrary web datasets you find on the internet, such as Kaggle competition datasets. You may refer to this tutorial or check out the fully compatible d8 dataset for loading custom datasets.

Loading data:

The dataset has internal DataFrame storage for easier access and analysis

Visualization:

similar for object detection:

gluoncv.auto.estimators

In this release, we packed the following high-level estimators for training and predicting images for image classification and object detection.

• gluoncv.auto.estimators.ImageClassificationEstimator
• gluoncv.auto.estimators.SSDEstimator
• gluoncv.auto.estimators.CenterNetEstimator
• gluoncv.auto.estimators.FasterRCNNEstimator
• gluoncv.auto.estimators.YOLOv3Estimator

Highlighted usages

• fit function:

• predict, predict_proba(for image classification), predict_feature(for image classification)

• save and load.

You may visit the tutorial website for more detailed examples.

gluoncv.auto.tasks

In this release, the following auto tasks are supported and have been massively tested on many datasets to ensure HPO performance:

• gluoncv.auto.tasks.ImageClassification
• gluoncv.auto.tasks.ObjectDetection

Comparing with pure algorithm-based estimators, the auto tasks provide identical APIs and functionalities but allow you to fit with hyper-parameter optimization(HPO) with specified num_trials and time_limit. For object detection, it allows multiple algorithms(e.g., SSDEstimator and FasterRCNNEstimator) to be tuned as a categorical search space.

The tutorial is available here

Bug fixes and improvements

• Improved training speed for mask-rcnn script (#1595, [#1609])
• Fix an issue in classification dataset (#1599)
• Fix a batch-size issue for mask-rcnn validation during training (#1594)
• Fix an os directory issue for model zoo folder (#1591)
• Improved CI stability (#1581)