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Improving model coverage
The primary goal of this repository is to provide a diverse range of models, covering many domains. This is useful both for testing coverage of Swift for TensorFlow, as well as for documenting how to implement various machine learning models.
For benchmarking purposes, it's important to us to have full coverage of the models and datasets used as part of the MLPerf training and MLPerf inference benchmark suites.
Image classification is pretty well covered, but novel architectures could still be welcome there. Other categories of models aren't as well represented, and help would be appreciated in building out those. The following list is some we've identified, but is by no means exhaustive:
- BERT on SQuAD (1.1 and 2.0)
- ALBERT on SQuAD
- Reformer
- U-Net (2-D)
- U-Net (3-D)
- StarNet on the Waymo Open Dataset
- Deep Speech 2 on LibriSpeech
- RNN-T
- GNMT
- Transformer on WMT English-German
- Neural Collaborative Filtering on MovieLens
- Seq-CNN on IMDB
- XLNet
- MiniGo (training)
Object detection: (currently being worked on by teams at Google) We're actively working on support for object detection models, so please hold off to avoid duplicate work. Thanks!