Updated readme

Author: lukemelas

README.md

@@ -1,5 +1,28 @@
 # EfficientNet PyTorch
 
+
+_IMPORTANT NOTE_: In the latest update, I switched hosting providers for the pretrained models, as the previous models were becoming extremely expensive to host. This _will_ break old versions of the library. I apologize, but I cannot afford to keep serving the models on the old provider. Everything should work properly if you update the library: 
+```
+pip install --upgrade efficientnet-pytorch
+```
+
+### Update (January 23, 2020)
+
+This update adds a new category of pre-trained model based on adversarial training, called _advprop_. It is important to note that the preprocessing required for the advprop pretrained models is slightly different from normal ImageNet preprocessing. As a result, by default, advprop models are not used. To load a model with advprop, use:
+```
+model = EfficientNet.from_pretrained("efficientnet-b0", advprop=True)
+```
+There is also a new, large `efficientnet-b8` pretrained model that is only available in advprop form. When using these models, replace ImageNet preprocessing code as follows:
+```
+if advprop:  # for models using advprop pretrained weights
+    normalize = transforms.Lambda(lambda img: img * 2.0 - 1.0)
+else:
+    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], 
+                                     std=[0.229, 0.224, 0.225])
+
+```
+This update also addresses multiple other issues ([#115](https://github.com/lukemelas/EfficientNet-PyTorch/issues/115), [#128](https://github.com/lukemelas/EfficientNet-PyTorch/issues/128)). 
+
 ### Update (October 15, 2019)
 
 This update allows you to choose whether to use a memory-efficient Swish activation. The memory-efficient version is chosen by default, but it cannot be used when exporting using PyTorch JIT. For this purpose, we have also included a standard (export-friendly) swish activation function. To switch to the export-friendly version, simply call `model.set_swish(memory_efficient=False)` after loading your desired model. This update addresses issues [#88](https://github.com/lukemelas/EfficientNet-PyTorch/pull/88) and [#89](https://github.com/lukemelas/EfficientNet-PyTorch/pull/89).