From 4808b3c32ffda5aa26c28e31a4b23ea6ef1969d1 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Mon, 3 Feb 2020 11:43:14 -0800 Subject: [PATCH] Bump version for PyPi update, fix few out of date README items/mistakes, add README updates for TF EfficientNet-B8 (RandAugment) --- README.md | 19 +++++++++++++------ timm/version.py | 2 +- 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index dfee0a23..afa5665e 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,11 @@ ## What's New +### Feb 1/2, 2020 +* Port new EfficientNet-B8 (RandAugment) weights, these are different than the B8 AdvProp, different input normalization. +* Update results csv files on all models for ImageNet validation and three other test sets +* Push PyPi package update + ### Jan 31, 2020 * Update ResNet50 weights with a new 79.038 result from further JSD / AugMix experiments. Full command line for reproduction in training section below. @@ -87,9 +92,9 @@ Included models: * Original variant from [Cadene](https://github.com/Cadene/pretrained-models.pytorch) * MXNet Gluon 'modified aligned' Xception-65 and 71 models from [Gluon ModelZoo](https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo) * PNasNet & NASNet-A (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch)) -* DPN (from [me](https://github.com/rwightman/pytorch-dpn-pretrained), weights hosted by Cadene) +* DPN (from [myself](https://github.com/rwightman/pytorch-dpn-pretrained)) * DPN-68, DPN-68b, DPN-92, DPN-98, DPN-131, DPN-107 -* EfficientNet (from my standalone [GenMobileNet](https://github.com/rwightman/genmobilenet-pytorch)) - A generic model that implements many of the efficient models that utilize similar DepthwiseSeparable and InvertedResidual blocks +* EfficientNet (from my standalone [GenEfficientNet](https://github.com/rwightman/gen-efficientnet-pytorch)) - A generic model that implements many of the efficient models that utilize similar DepthwiseSeparable and InvertedResidual blocks * EfficientNet AdvProp (B0-B8) (https://arxiv.org/abs/1911.09665) -- TF weights ported * EfficientNet (B0-B7) (https://arxiv.org/abs/1905.11946) -- TF weights ported, B0-B2 finetuned PyTorch * EfficientNet-EdgeTPU (S, M, L) (https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html) --TF weights ported @@ -136,8 +141,8 @@ I've leveraged the training scripts in this repository to train a few of the mod |Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Image Size | |---|---|---|---|---|---| -| efficientnet_b3a | 81.874 (18.126) | 95.840 (4.160) | 9.11M | bicubic | 320 (1.0 crop) | -| efficientnet_b3 | 81.498 (18.502) | 95.718 (4.282) | 9.11M | bicubic | 300 | +| efficientnet_b3a | 81.874 (18.126) | 95.840 (4.160) | 12.23M | bicubic | 320 (1.0 crop) | +| efficientnet_b3 | 81.498 (18.502) | 95.718 (4.282) | 12.23M | bicubic | 300 | | efficientnet_b2a | 80.608 (19.392) | 95.310 (4.690) | 9.11M | bicubic | 288 (1.0 crop) | | mixnet_xl | 80.478 (19.522) | 94.932 (5.068) | 11.90M | bicubic | 224 | | efficientnet_b2 | 80.402 (19.598) | 95.076 (4.924) | 9.11M | bicubic | 260 | @@ -170,6 +175,8 @@ For the models below, the model code and weight porting from Tensorflow or MXNet | Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Image Size | |---|---|---|---|---|---| | tf_efficientnet_b8_ap *tfp | 85.436 (14.564) | 97.272 (2.728) | 87.4 | bicubic | 672 | +| tf_efficientnet_b8 *tfp | 85.384 (14.616) | 97.394 (2.606) | 87.4 | bicubic | 672 | +| tf_efficientnet_b8 | 85.37 (14.63) | 97.39 (2.61) | 87.4 | bicubic | 672 | | tf_efficientnet_b8_ap | 85.368 (14.632) | 97.294 (2.706) | 87.4 | bicubic | 672 | | tf_efficientnet_b7_ap *tfp | 85.154 (14.846) | 97.244 (2.756) | 66.35 | bicubic | 600 | | tf_efficientnet_b7_ap | 85.118 (14.882) | 97.252 (2.748) | 66.35 | bicubic | 600 | @@ -309,13 +316,13 @@ Trained on two older 1080Ti cards, this took a while. Only slightly, non statist 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. Little to no care has been taken to be Python 2.x friendly and I don't plan to support it. If you run into any challenges running on Windows, or other OS, I'm definitely open to looking into those issues so long as it's in a reproducible (read Conda) environment. -PyTorch versions 1.2 and 1.3.1 have been tested with this code. +PyTorch versions 1.2, 1.3.1, and 1.4 have been tested with this code. I've tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda: ``` conda create -n torch-env conda activate torch-env -conda install -c pytorch pytorch torchvision cudatoolkit=10 +conda install -c pytorch pytorch torchvision cudatoolkit=10.1 conda install pyyaml ``` diff --git a/timm/version.py b/timm/version.py index 112abf13..b7e15fae 100644 --- a/timm/version.py +++ b/timm/version.py @@ -1 +1 @@ -__version__ = '0.1.14' +__version__ = '0.1.16'