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Ross Wightman
4 years ago
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<h1>Changes</h1>
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||||
<h3 id="june-11-2020">June 11, 2020</h3>
|
||||
<p>Bunch of changes:</p>
|
||||
<ul>
|
||||
<li>DenseNet models updated with memory efficient addition from torchvision (fixed a bug), blur pooling and deep stem additions</li>
|
||||
<li>VoVNet V1 and V2 models added, 39 V2 variant (ese_vovnet_39b) trained to 79.3 top-1</li>
|
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<li>Activation factory added along with new activations:</li>
|
||||
<li>select act at model creation time for more flexibility in using activations compatible with scripting or tracing (ONNX export)</li>
|
||||
<li>hard_mish (experimental) added with memory-efficient grad, along with ME hard_swish</li>
|
||||
<li>context mgr for setting exportable/scriptable/no_jit states</li>
|
||||
<li>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</li>
|
||||
<li>Torchscript works for all but two of the model types as long as using Pytorch 1.5+, tests added for this</li>
|
||||
<li>Some import cleanup and classifier reset changes, all models will have classifier reset to nn.Identity on reset_classifer(0) call</li>
|
||||
<li>Prep for 0.1.28 pip release</li>
|
||||
</ul>
|
||||
<h3 id="may-12-2020">May 12, 2020</h3>
|
||||
<ul>
|
||||
<li>Add ResNeSt models (code adapted from <a href="https://github.com/zhanghang1989/ResNeSt">https://github.com/zhanghang1989/ResNeSt</a>, paper <a href="https://arxiv.org/abs/2004.08955">https://arxiv.org/abs/2004.08955</a>))</li>
|
||||
</ul>
|
||||
<h3 id="may-3-2020">May 3, 2020</h3>
|
||||
<ul>
|
||||
<li>Pruned EfficientNet B1, B2, and B3 (<a href="https://arxiv.org/abs/2002.08258">https://arxiv.org/abs/2002.08258</a>) contributed by <a href="https://github.com/yoniaflalo">Yonathan Aflalo</a></li>
|
||||
</ul>
|
||||
<h3 id="may-1-2020">May 1, 2020</h3>
|
||||
<ul>
|
||||
<li>Merged a number of execellent contributions in the ResNet model family over the past month</li>
|
||||
<li>BlurPool2D and resnetblur models initiated by <a href="https://github.com/VRandme">Chris Ha</a>, I trained resnetblur50 to 79.3.</li>
|
||||
<li>TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by <a href="https://github.com/mrT23">mrT23</a></li>
|
||||
<li>ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per (<a href="https://arxiv.org/abs/2002.08258">https://arxiv.org/abs/2002.08258</a>) by <a href="https://github.com/yoniaflalo">Yonathan Aflalo</a></li>
|
||||
<li>200 pretrained models in total now with updated results csv in results folder</li>
|
||||
</ul>
|
||||
<h3 id="april-5-2020">April 5, 2020</h3>
|
||||
<ul>
|
||||
<li>Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite</li>
|
||||
<li>3.5M param MobileNet-V2 100 @ 73%</li>
|
||||
<li>4.5M param MobileNet-V2 110d @ 75%</li>
|
||||
<li>6.1M param MobileNet-V2 140 @ 76.5%</li>
|
||||
<li>5.8M param MobileNet-V2 120d @ 77.3%</li>
|
||||
</ul>
|
||||
<h3 id="march-18-2020">March 18, 2020</h3>
|
||||
<ul>
|
||||
<li>Add EfficientNet-Lite models w/ weights ported from <a href="https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite">Tensorflow TPU</a></li>
|
||||
<li>Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Trained by <a href="https://github.com/andravin">Andrew Lavin</a> (see Training section for hparams)</li>
|
||||
</ul>
|
||||
<h3 id="feb-29-2020">Feb 29, 2020</h3>
|
||||
<ul>
|
||||
<li>New MobileNet-V3 Large weights trained from stratch with this code to 75.77% top-1</li>
|
||||
<li>IMPORTANT CHANGE - default weight init changed for all MobilenetV3 / EfficientNet / related models</li>
|
||||
<li>overall results similar to a bit better training from scratch on a few smaller models tried</li>
|
||||
<li>performance early in training seems consistently improved but less difference by end</li>
|
||||
<li>set <code>fix_group_fanout=False</code> in <code>_init_weight_goog</code> fn if you need to reproducte past behaviour</li>
|
||||
<li>Experimental LR noise feature added applies a random perturbation to LR each epoch in specified range of training</li>
|
||||
</ul>
|
||||
<h3 id="feb-18-2020">Feb 18, 2020</h3>
|
||||
<ul>
|
||||
<li>Big refactor of model layers and addition of several attention mechanisms. Several additions motivated by 'Compounding the Performance Improvements...' (<a href="https://arxiv.org/abs/2001.06268">https://arxiv.org/abs/2001.06268</a>):</li>
|
||||
<li>Move layer/module impl into <code>layers</code> subfolder/module of <code>models</code> and organize in a more granular fashion</li>
|
||||
<li>ResNet downsample paths now properly support dilation (output stride != 32) for avg_pool ('D' variant) and 3x3 (SENets) networks</li>
|
||||
<li>Add Selective Kernel Nets on top of ResNet base, pretrained weights<ul>
|
||||
<li>skresnet18 - 73% top-1</li>
|
||||
<li>skresnet34 - 76.9% top-1 </li>
|
||||
<li>skresnext50_32x4d (equiv to SKNet50) - 80.2% top-1</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>ECA and CECA (circular padding) attention layer contributed by <a href="https://github.com/VRandme">Chris Ha</a></li>
|
||||
<li>CBAM attention experiment (not the best results so far, may remove)</li>
|
||||
<li>Attention factory to allow dynamically selecting one of SE, ECA, CBAM in the <code>.se</code> position for all ResNets</li>
|
||||
<li>Add DropBlock and DropPath (formerly DropConnect for EfficientNet/MobileNetv3) support to all ResNet variants</li>
|
||||
<li>Full dataset results updated that incl NoisyStudent weights and 2 of the 3 SK weights</li>
|
||||
</ul>
|
||||
<h3 id="feb-12-2020">Feb 12, 2020</h3>
|
||||
<ul>
|
||||
<li>Add EfficientNet-L2 and B0-B7 NoisyStudent weights ported from <a href="https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet">Tensorflow TPU</a></li>
|
||||
</ul>
|
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<h1 id="getting-started">Getting Started</h1>
|
||||
<h2 id="install">Install</h2>
|
||||
<p>The library can be installed with pip:</p>
|
||||
<div class="highlight"><pre><span></span><code>pip install timm
|
||||
</code></pre></div>
|
||||
|
||||
<div class="admonition info">
|
||||
<p class="admonition-title">Conda Environment</p>
|
||||
<p>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. </p>
|
||||
<p>To install <code>timm</code> in a conda environment:
|
||||
<div class="highlight"><pre><span></span><code>conda create -n torch-env
|
||||
conda activate torch-env
|
||||
conda install -c pytorch pytorch torchvision cudatoolkit=10.1
|
||||
conda install pyyaml
|
||||
pip install timm
|
||||
</code></pre></div></p>
|
||||
</div>
|
||||
<h2 id="load-pretrained-model">Load Pretrained Model</h2>
|
||||
<p>Pretrained models can be loaded using <code>timm.create_model</code></p>
|
||||
<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">timm</span>
|
||||
|
||||
<span class="n">m</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">'mobilenetv3_100'</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
|
||||
<span class="n">m</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
|
||||
</code></pre></div>
|
||||
|
||||
<p>To load a different model see <a href="/models
|
||||
/#pretrained-imagenet-weights">the list of pretrained weights</a>.</p>
|
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