v0.2.0

🗞️ What's new

• 🎾 Matching Networks
• 🧬 Relation Networks
• 🗻 tieredImageNet
• 🌼 In AbtractMetaLearner and all children classes, forward()now takes only query_images as argument. Support images and labels are now processed by process_support_set().
• 📈 AbstractMetaLearner.fit() now allows validation on a validation set.
• 🌈 EasySet.__getitem__() now forces loaded images conversion to RGB.
• ✔️ The code is tested

Initial release

The initial release contains :

• AbstractMetaLearner: an abstract class with methods that can be used for any meta-trainable algorithm
• Prototypical Networks
• EasySet: a ready-to-use Dataset object to handle datasets of images with a class-wise directory split
• TaskSampler: samples batches in the shape of few-shot classification tasks
• CU-Birds: we provide a script to download and extract the dataset, along with a meta-train/meta-val/meta-test split along classes. The dataset is ready-to-use with EasySet.