Merge branch 'michalwols-docs'

Author: rwightman

docs/changes.md

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+### June 11, 2020
+Bunch of changes:
+
+* DenseNet models updated with memory efficient addition from torchvision (fixed a bug), blur pooling and deep stem additions
+* VoVNet V1 and V2 models added, 39 V2 variant (ese_vovnet_39b) trained to 79.3 top-1
+* Activation factory added along with new activations:
+   * select act at model creation time for more flexibility in using activations compatible with scripting or tracing (ONNX export)
+   * hard_mish (experimental) added with memory-efficient grad, along with ME hard_swish
+   * context mgr for setting exportable/scriptable/no_jit states
+* Norm + Activation combo layers added with initial trial support in DenseNet and VoVNet along with impl of EvoNorm and InplaceAbn wrapper that fit the interface
+* Torchscript works for all but two of the model types as long as using Pytorch 1.5+, tests added for this
+* Some import cleanup and classifier reset changes, all models will have classifier reset to nn.Identity on reset_classifer(0) call
+* Prep for 0.1.28 pip release
+
+### May 12, 2020
+* Add ResNeSt models (code adapted from https://github.com/zhanghang1989/ResNeSt, paper https://arxiv.org/abs/2004.08955))
+
+### May 3, 2020
+* Pruned EfficientNet B1, B2, and B3 (https://arxiv.org/abs/2002.08258) contributed by [Yonathan Aflalo](https://github.com/yoniaflalo)
+
+### May 1, 2020
+* Merged a number of execellent contributions in the ResNet model family over the past month
+  * BlurPool2D and resnetblur models initiated by [Chris Ha](https://github.com/VRandme), I trained resnetblur50 to 79.3.
+  * TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by [mrT23](https://github.com/mrT23)
+  * ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per (https://arxiv.org/abs/2002.08258) by [Yonathan Aflalo](https://github.com/yoniaflalo)
+* 200 pretrained models in total now with updated results csv in results folder
+
+### April 5, 2020
+* Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite
+  * 3.5M param MobileNet-V2 100 @ 73%
+  * 4.5M param MobileNet-V2 110d @ 75%
+  * 6.1M param MobileNet-V2 140 @ 76.5%
+  * 5.8M param MobileNet-V2 120d @ 77.3%
+
+### March 18, 2020
+* Add EfficientNet-Lite models w/ weights ported from [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite)
+* Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Trained by [Andrew Lavin](https://github.com/andravin) (see Training section for hparams)
+
+### Feb 29, 2020
+* New MobileNet-V3 Large weights trained from stratch with this code to 75.77% top-1
+* IMPORTANT CHANGE - default weight init changed for all MobilenetV3 / EfficientNet / related models
+  * overall results similar to a bit better training from scratch on a few smaller models tried
+  * performance early in training seems consistently improved but less difference by end
+  * set `fix_group_fanout=False` in `_init_weight_goog` fn if you need to reproducte past behaviour
+* Experimental LR noise feature added applies a random perturbation to LR each epoch in specified range of training
+
+### Feb 18, 2020
+* Big refactor of model layers and addition of several attention mechanisms. Several additions motivated by 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268):
+  * Move layer/module impl into `layers` subfolder/module of `models` and organize in a more granular fashion
+  * ResNet downsample paths now properly support dilation (output stride != 32) for avg_pool ('D' variant) and 3x3 (SENets) networks
+  * Add Selective Kernel Nets on top of ResNet base, pretrained weights
+    * skresnet18 - 73% top-1
+    * skresnet34 - 76.9% top-1 
+    * skresnext50_32x4d (equiv to SKNet50) - 80.2% top-1
+  * ECA and CECA (circular padding) attention layer contributed by [Chris Ha](https://github.com/VRandme)
+  * CBAM attention experiment (not the best results so far, may remove)
+  * Attention factory to allow dynamically selecting one of SE, ECA, CBAM in the `.se` position for all ResNets
+  * Add DropBlock and DropPath (formerly DropConnect for EfficientNet/MobileNetv3) support to all ResNet variants
+* Full dataset results updated that incl NoisyStudent weights and 2 of the 3 SK weights
+
+### Feb 12, 2020
+* Add EfficientNet-L2 and B0-B7 NoisyStudent weights ported from [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet)

docs/index.md

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+# Getting Started
+
+
+## Install
+
+The library can be installed with pip:
+
+```
+pip install timm
+```
+
+!!! info "Conda Environment"
+    All development and testing has been done in Conda Python 3 environments
+     on Linux x86-64 systems, specifically Python 3.6.x and 3.7.x. 
+
+    To install `timm` in a conda environment:
+    ```
+    conda create -n torch-env
+    conda activate torch-env
+    conda install -c pytorch pytorch torchvision cudatoolkit=10.1
+    conda install pyyaml
+    pip install timm
+    ```
+
+
+## Load Pretrained Model
+
+Pretrained models can be loaded using `timm.create_model`
+
+```python
+import timm
+
+m = timm.create_model('mobilenetv3_100', pretrained=True)
+m.eval()
+```
+
+To load a different model see [the list of pretrained weights](/models
+/#pretrained-imagenet-weights).