8 Commits (c6b8259ad60965c7e260f76d5d59625a351b2b07)

Author	SHA1	Message	Date
Ross Wightman	99122aac1c	Replace ResNet-34 default weights with a great result from my experiments.	6 years ago
Ross Wightman	6bff9c75dc	Cleanup model_factory imports, consistent __all__ for models, fixed inception_v4 weight url	6 years ago
Ross Wightman	76539d905e	Some transform/data/loader refactoring, hopefully didn't break things ... * factor out data related constants to own file * move data related config helpers to own file * add a variant of RandomResizeCrop that randomizes interpolation method * remove old Numpy version of RandomErasing * cleanup torch version of RandomErasing and use it in either GPU loader batch mode or single image cpu Transform	6 years ago
Ross Wightman	8a33a6c90a	Add checkpoint clean script, add link to pretrained resnext50 weights	6 years ago
Ross Wightman	0562b91c38	Add per model crop pct, interpolation defaults, tie it all together ... * create one resolve fn to pull together model defaults + cmd line args * update attribution comments in some models * test update train/validation/inference scripts	6 years ago
Ross Wightman	9c3859fb9c	Uniform pretrained model handling. ... * All models have 'default_cfgs' dict * load/resume/pretrained helpers factored out * pretrained load operates on state_dict based on default_cfg * test all models in validate * schedule, optim factor factored out * test time pool wrapper applied based on default_cfg	6 years ago
Ross Wightman	0bc50e84f8	Lots of refactoring and cleanup. ... * Move 'test time pool' to Module that can be used by any model, remove from DPN * Remove ResNext model file and combine with ResNet * Remove fbresnet200 as it was an old conversion and pretrained performance not worth param count * Cleanup adaptive avgmax pooling and add back conctat variant * Factor out checkpoint load fn	6 years ago
Ross Wightman	e0cfeb7d8e	Add some models, remove a model, tweak some models	6 years ago