Add BiT references and knowledge distill links to readme/docs

README.md

@@ -130,6 +130,7 @@ All model architecture families include variants with pretrained weights. The ar
 
 A full version of the list below with source links can be found in the [documentation](https://rwightman.github.io/pytorch-image-models/models/).
 
+* Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
 * CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
 * DenseNet - https://arxiv.org/abs/1608.06993
 * DLA - https://arxiv.org/abs/1707.06484
@@ -242,6 +243,10 @@ One of the greatest assets of PyTorch is the community and their contributions.
 * Albumentations - https://github.com/albumentations-team/albumentations
 * Kornia - https://github.com/kornia/kornia
 
+### Knowledge Distillation
+* RepDistiller - https://github.com/HobbitLong/RepDistiller
+* torchdistill - https://github.com/yoshitomo-matsubara/torchdistill
+
 ### Metric Learning
 * PyTorch Metric Learning - https://github.com/KevinMusgrave/pytorch-metric-learning
 

docs/models.md

@@ -10,6 +10,10 @@ Most included models have pretrained weights. The weights are either:
 
 The validation results for the pretrained weights can be found [here](results.md)
 
+## Big Transfer ResNetV2 (BiT) [[resnetv2.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnetv2.py)]
+* Paper: `Big Transfer (BiT): General Visual Representation Learning` - https://arxiv.org/abs/1912.11370
+* Reference code: https://github.com/google-research/big_transfer
+
 ## Cross-Stage Partial Networks [[cspnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/cspnet.py)]
 * Paper: `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929
 * Reference impl: https://github.com/WongKinYiu/CrossStagePartialNetworks