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A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

v0.3.1

4 years ago

v0.3

4 years ago

Features

  • Multi-tasking support: Multitasking over various datasets available in Pythia
  • Distributed training support
  • Better Customization Support: Use your custom losses, metrics, optimizers and lot of more
  • Standardized Trainer API: A standard trainer API to fit most of your use-cases, if not inherit and build your own trainer
  • Processors: Use processors to build out your datasets easily and without pain
  • SampleList and Sample: Use SampleList and Sample to have more granular control over what you pass and a single unified API for accessing attributes whether inside a dataset, a single sample or a batch
  • Feature Extraction: A new simple script to extract out features and related information from VQA MaskRCNN Benchmark
  • Registry: No need to manually load datasets and models anymore, registry takes care of loading your models, datasets and other classes at the fly. Think of registry as a singleton containing all that you need.
  • Tensorboard Logging: Tensorboard logging is now provided by default.
  • Configuration: Better hierarchal configuration system for better separation of concerns.
  • Checkpointing: Better control checkpointing and resuming
  • Logging: Better logging is provided now with eta, individual val losses and metrics. Just pass them back from your model and everything logs automatically
  • EvalAI Evaluation: Now, directly output JSON files that can be uploaded to EvalAI
  • Early Stopping
  • New Embeddings Support: FastText, GloVe, BERT etc.

Datasets

Following new datasets were added:

Models

Note: There are a lot of breaking changes in the API from v0.1. Refer to the documentation to learn more on how to work with Pythia v0.3.