We have added a large set of baseline results and pretrained models available for download in the pycls Model Zoo; including the simple, fast, and effective RegNet models that we hope can serve as solid baselines across a wide range of flop regimes.
New features included in this release:
- Cache model weight URLs provided in configs locally | 4e470e21ad55aff941f1098666f70ef982626f7a
- Allow optional weight decay fine-tuning for BN params, changes the default BN weight decay | 4e470e21ad55aff941f1098666f70ef982626f7a
- Support Squeeze & Excitation in AnyNet | ec188632fab607a41bff1a63c7ac9bd0d949bb52
- RegNet model abstraction | 4f5b5dafe4f4274f7cba774923b8d1ba81d9904c, 708d429e67b1a43dfd7e22cb754ecaaf234ba308, 24a2805f1040787a047ed0339925adf98109e64c
- Run precise time in test_net and count acts for a model | ad01b81c516e59e134bfde22ab827aa61744b83c
Other changes:
- Updated README | 4acac2b06945f6b0722f20748e2104bd6a771468
- New Model Zoo | 4acac2b06945f6b0722f20748e2104bd6a771468, 4f5b5dafe4f4274f7cba774923b8d1ba81d9904c
- New configs for models from Designing Network Design Spaces paper | 44bcfa71ebd7b9cd8e81f4d28ce319e814cbbb73, e9c1ef4583ef089c182dd7fe9823db063a4909e3