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294 lines
22 KiB
294 lines
22 KiB
# PyTorch Image Models, etc
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## What's New
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### Dec 4, 2019
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* Added weights from the first training from scratch of an EfficientNet (B2) with my new RandAugment implementation. Much better than my previous B2 and very close to the official AdvProp ones (80.4 top-1, 95.08 top-5).
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* For those interested in hparams, I trained with the following: `./distributed_train.sh 2 /imagenet/ --model efficientnet_b2 -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-noise0.5 --remode pixel --reprob 0.2 --amp --lr .016`
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### Nov 29, 2019
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* Brought EfficientNet and MobileNetV3 up to date with my https://github.com/rwightman/gen-efficientnet-pytorch code. Torchscript and ONNX export compat excluded.
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* AdvProp weights added
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* Official TF MobileNetv3 weights added
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* EfficientNet and MobileNetV3 hook based 'feature extraction' classes added. Will serve as basis for using models as backbones in obj detection/segmentation tasks. Lots more to be done here...
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* HRNet classification models and weights added from https://github.com/HRNet/HRNet-Image-Classification
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* Consistency in global pooling, `reset_classifer`, and `forward_features` across models
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* `forward_features` always returns unpooled feature maps now
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* Reasonable chance I broke something... let me know
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### Nov 22, 2019
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* Add ImageNet training RandAugment implementation alongside AutoAugment. PyTorch Transform compatible format, using PIL. Currently training two EfficientNet models from scratch with promising results... will update.
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* `drop-connect` cmd line arg finally added to `train.py`, no need to hack model fns. Works for efficientnet/mobilenetv3 based models, ignored otherwise.
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## Introduction
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For each competition, personal, or freelance project involving images + Convolution Neural Networks, I build on top of an evolving collection of code and models. This repo contains a (somewhat) cleaned up and paired down iteration of that code. Hopefully it'll be of use to others.
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The work of many others is present here. I've tried to make sure all source material is acknowledged:
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* Training/validation scripts evolved from early versions of the [PyTorch Imagenet Examples](https://github.com/pytorch/examples)
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* CUDA specific performance enhancements have been pulled from [NVIDIA's APEX Examples](https://github.com/NVIDIA/apex/tree/master/examples)
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* Models are from a wide variety of sources
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* [Torchvision](https://github.com/pytorch/vision/tree/master/torchvision/models)
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* [Cadene's Pretrained Models](https://github.com/Cadene/pretrained-models.pytorch)
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* [Myself](https://github.com/rwightman/pytorch-dpn-pretrained)
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* LR scheduler ideas from [AllenNLP](https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers), [FAIRseq](https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler), and SGDR: Stochastic Gradient Descent with Warm Restarts (https://arxiv.org/abs/1608.03983)
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* Random Erasing from [Zhun Zhong](https://github.com/zhunzhong07/Random-Erasing/blob/master/transforms.py) (https://arxiv.org/abs/1708.04896)
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* Optimizers:
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* RAdam by [Liyuan Liu](https://github.com/LiyuanLucasLiu/RAdam) (https://arxiv.org/abs/1908.03265)
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* NovoGrad by [Masashi Kimura](https://github.com/convergence-lab/novograd) (https://arxiv.org/abs/1905.11286)
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* Lookahead adapted from impl by [Liam](https://github.com/alphadl/lookahead.pytorch) (https://arxiv.org/abs/1907.08610)
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## Models
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I've included a few of my favourite models, but this is not an exhaustive collection. You can't do better than Cadene's collection in that regard. Most models do have pretrained weights from their respective sources or original authors.
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* ResNet/ResNeXt (from [torchvision](https://github.com/pytorch/vision/tree/master/torchvision/models) with mods by myself)
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* ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, ResNeXt50 (32x4d), ResNeXt101 (32x4d and 64x4d)
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* 'Bag of Tricks' / Gluon C, D, E, S variations (https://arxiv.org/abs/1812.01187)
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* Instagram trained / ImageNet tuned ResNeXt101-32x8d to 32x48d from from [facebookresearch](https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/)
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* Res2Net (https://github.com/gasvn/Res2Net, https://arxiv.org/abs/1904.01169)
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* DLA
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* Original (https://github.com/ucbdrive/dla, https://arxiv.org/abs/1707.06484)
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* Res2Net (https://github.com/gasvn/Res2Net, https://arxiv.org/abs/1904.01169)
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* DenseNet (from [torchvision](https://github.com/pytorch/vision/tree/master/torchvision/models))
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* DenseNet-121, DenseNet-169, DenseNet-201, DenseNet-161
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* Squeeze-and-Excitation ResNet/ResNeXt (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch) with some pretrained weight additions by myself)
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* SENet-154, SE-ResNet-18, SE-ResNet-34, SE-ResNet-50, SE-ResNet-101, SE-ResNet-152, SE-ResNeXt-26 (32x4d), SE-ResNeXt50 (32x4d), SE-ResNeXt101 (32x4d)
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* Inception-ResNet-V2 and Inception-V4 (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch) )
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* Xception
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* Original variant from [Cadene](https://github.com/Cadene/pretrained-models.pytorch)
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* MXNet Gluon 'modified aligned' Xception-65 and 71 models from [Gluon ModelZoo](https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo)
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* PNasNet & NASNet-A (from [Cadene](https://github.com/Cadene/pretrained-models.pytorch))
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* DPN (from [me](https://github.com/rwightman/pytorch-dpn-pretrained), weights hosted by Cadene)
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* DPN-68, DPN-68b, DPN-92, DPN-98, DPN-131, DPN-107
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* 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
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* EfficientNet AdvProp (B0-B8) (https://arxiv.org/abs/1911.09665) -- TF weights ported
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* EfficientNet (B0-B7) (https://arxiv.org/abs/1905.11946) -- TF weights ported, B0-B2 finetuned PyTorch
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* EfficientNet-EdgeTPU (S, M, L) (https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html) --TF weights ported
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* MixNet (https://arxiv.org/abs/1907.09595) -- TF weights ported, PyTorch finetuned (S, M, L) or trained models (XL)
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* MNASNet B1, A1 (Squeeze-Excite), and Small (https://arxiv.org/abs/1807.11626) -- trained in PyTorch
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* MobileNet-V2 (https://arxiv.org/abs/1801.04381)
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* FBNet-C (https://arxiv.org/abs/1812.03443) -- trained in PyTorch
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* Single-Path NAS (https://arxiv.org/abs/1904.02877) -- pixel1 variant
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* MobileNet-V3 (https://arxiv.org/abs/1905.02244) -- pretrained PyTorch model, official TF weights ported
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* HRNet
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* code from https://github.com/HRNet/HRNet-Image-Classification, paper https://arxiv.org/abs/1908.07919
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Use the `--model` arg to specify model for train, validation, inference scripts. Match the all lowercase
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creation fn for the model you'd like.
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## Features
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Several (less common) features that I often utilize in my projects are included. Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP:
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* All models have a common default configuration interface and API for
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* accessing/changing the classifier - `get_classifier` and `reset_classifier`
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* doing a forward pass on just the features - `forward_features`
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* these makes it easy to write consistent network wrappers that work with any of the models
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* All models have a consistent pretrained weight loader that adapts last linear if necessary, and from 3 to 1 channel input if desired
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* The train script works in several process/GPU modes:
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* NVIDIA DDP w/ a single GPU per process, multiple processes with APEX present (AMP mixed-precision optional)
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* PyTorch DistributedDataParallel w/ multi-gpu, single process (AMP disabled as it crashes when enabled)
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* PyTorch w/ single GPU single process (AMP optional)
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* A dynamic global pool implementation that allows selecting from average pooling, max pooling, average + max, or concat([average, max]) at model creation. All global pooling is adaptive average by default and compatible with pretrained weights.
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* A 'Test Time Pool' wrapper that can wrap any of the included models and usually provide improved performance doing inference with input images larger than the training size. Idea adapted from original DPN implementation when I ported (https://github.com/cypw/DPNs)
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* Training schedules and techniques that provide competitive results (Cosine LR, Random Erasing, Label Smoothing, etc)
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* Mixup (as in https://arxiv.org/abs/1710.09412) - currently implementing/testing
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* An inference script that dumps output to CSV is provided as an example
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* AutoAugment (https://arxiv.org/abs/1805.09501) and RandAugment (https://arxiv.org/abs/1909.13719) ImageNet configurations modeled after impl for EfficientNet training (https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py)
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## Results
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A CSV file containing an ImageNet-1K validation results summary for all included models with pretrained weights and default configurations is located [here](results/results-all.csv)
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### Self-trained Weights
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I've leveraged the training scripts in this repository to train a few of the models with missing weights to good levels of performance. These numbers are all for 224x224 training and validation image sizing with the usual 87.5% validation crop.
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|Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Image Size |
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| efficientnet_b2 | 80.402 (19.598) | 95.076 (4.924) | 9.11M | bicubic | 260 |
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| mixnet_xl | 80.120 (19.880) | 95.022 (4.978) | 11.90M | bicubic | 224 |
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| resnext50d_32x4d | 79.674 (20.326) | 94.868 (5.132) | 25.1M | bicubic | 224 |
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| mixnet_l | 78.976 (21.024 | 94.184 (5.816) | 7.33M | bicubic | 224 |
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| efficientnet_b1 | 78.692 (21.308) | 94.086 (5.914) | 7.79M | bicubic | 240 |
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| resnext50_32x4d | 78.512 (21.488) | 94.042 (5.958) | 25M | bicubic | 224 |
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| resnet50 | 78.470 (21.530) | 94.266 (5.734) | 25.6M | bicubic | 224 |
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| mixnet_m | 77.256 (22.744) | 93.418 (6.582) | 5.01M | bicubic | 224 |
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| seresnext26_32x4d | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic | 224 |
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| efficientnet_b0 | 76.912 (23.088) | 93.210 (6.790) | 5.29M | bicubic | 224 |
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| resnet26d | 76.68 (23.32) | 93.166 (6.834) | 16M | bicubic | 224 |
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| mixnet_s | 75.988 (24.012) | 92.794 (7.206) | 4.13M | bicubic | 224 |
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| mobilenetv3_100 | 75.634 (24.366) | 92.708 (7.292) | 5.5M | bicubic | 224 |
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| mnasnet_a1 | 75.448 (24.552) | 92.604 (7.396) | 3.89M | bicubic | 224 |
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| resnet26 | 75.292 (24.708) | 92.57 (7.43) | 16M | bicubic | 224 |
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| fbnetc_100 | 75.124 (24.876) | 92.386 (7.614) | 5.6M | bilinear | 224 |
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| resnet34 | 75.110 (24.890) | 92.284 (7.716) | 22M | bilinear | 224 |
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| seresnet34 | 74.808 (25.192) | 92.124 (7.876) | 22M | bilinear | 224 |
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| mnasnet_b1 | 74.658 (25.342) | 92.114 (7.886) | 4.38M | bicubic | 224 |
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| spnasnet_100 | 74.084 (25.916) | 91.818 (8.182) | 4.42M | bilinear | 224 |
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| seresnet18 | 71.742 (28.258) | 90.334 (9.666) | 11.8M | bicubic | 224 |
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### Ported Weights
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For the models below, the model code and weight porting from Tensorflow or MXNet Gluon to Pytorch was done by myself. There are weights/models ported by others included in this repository, they are not listed below.
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| Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Image Size |
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| tf_efficientnet_b8_ap *tfp | 85.436 (14.564) | 97.272 (2.728) | 87.4 | bicubic | 672 |
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| tf_efficientnet_b8_ap | 85.368 (14.632) | 97.294 (2.706) | 87.4 | bicubic | 672 |
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| tf_efficientnet_b7_ap *tfp | 85.154 (14.846) | 97.244 (2.756) | 66.35 | bicubic | 600 |
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| tf_efficientnet_b7_ap | 85.118 (14.882) | 97.252 (2.748) | 66.35 | bicubic | 600 |
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| tf_efficientnet_b7 *tfp | 84.940 (15.060) | 97.214 (2.786) | 66.35 | bicubic | 600 |
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| tf_efficientnet_b7 | 84.932 (15.068) | 97.208 (2.792) | 66.35 | bicubic | 600 |
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| tf_efficientnet_b6_ap | 84.786 (15.214) | 97.138 (2.862) | 43.04 | bicubic | 528 |
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| tf_efficientnet_b6_ap *tfp | 84.760 (15.240) | 97.124 (2.876) | 43.04 | bicubic | 528 |
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| tf_efficientnet_b5_ap *tfp | 84.276 (15.724) | 96.932 (3.068) | 30.39 | bicubic | 456 |
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| tf_efficientnet_b5_ap | 84.254 (15.746) | 96.976 (3.024) | 30.39 | bicubic | 456 |
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| tf_efficientnet_b6 *tfp | 84.140 (15.860) | 96.852 (3.148) | 43.04 | bicubic | 528 |
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| tf_efficientnet_b6 | 84.110 (15.890) | 96.886 (3.114) | 43.04 | bicubic | 528 |
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| tf_efficientnet_b5 *tfp | 83.822 (16.178) | 96.756 (3.244) | 30.39 | bicubic | 456 |
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| tf_efficientnet_b5 | 83.812 (16.188) | 96.748 (3.252) | 30.39 | bicubic | 456 |
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| tf_efficientnet_b4_ap *tfp | 83.278 (16.722) | 96.376 (3.624) | 19.34 | bicubic | 380 |
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| tf_efficientnet_b4_ap | 83.248 (16.752) | 96.388 (3.612) | 19.34 | bicubic | 380 |
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| tf_efficientnet_b4 | 83.022 (16.978) | 96.300 (3.700) | 19.34 | bicubic | 380 |
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| tf_efficientnet_b4 *tfp | 82.948 (17.052) | 96.308 (3.692) | 19.34 | bicubic | 380 |
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| tf_efficientnet_b3_ap *tfp | 81.882 (18.118) | 95.662 (4.338) | 12.23 | bicubic | 300 |
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| tf_efficientnet_b3_ap | 81.828 (18.172) | 95.624 (4.376) | 12.23 | bicubic | 300 |
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| tf_efficientnet_b3 | 81.636 (18.364) | 95.718 (4.282) | 12.23 | bicubic | 300 |
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| tf_efficientnet_b3 *tfp | 81.576 (18.424) | 95.662 (4.338) | 12.23 | bicubic | 300 |
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| gluon_senet154 | 81.224 (18.776) | 95.356 (4.644) | 115.09 | bicubic | 224 |
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| gluon_resnet152_v1s | 81.012 (18.988) | 95.416 (4.584) | 60.32 | bicubic | 224 |
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| gluon_seresnext101_32x4d | 80.902 (19.098) | 95.294 (4.706) | 48.96 | bicubic | 224 |
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| gluon_seresnext101_64x4d | 80.890 (19.110) | 95.304 (4.696) | 88.23 | bicubic | 224 |
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| gluon_resnext101_64x4d | 80.602 (19.398) | 94.994 (5.006) | 83.46 | bicubic | 224 |
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| tf_efficientnet_el | 80.534 (19.466) | 95.190 (4.810) | 10.59 | bicubic | 300 |
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| tf_efficientnet_el *tfp | 80.476 (19.524) | 95.200 (4.800) | 10.59 | bicubic | 300 |
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| gluon_resnet152_v1d | 80.470 (19.530) | 95.206 (4.794) | 60.21 | bicubic | 224 |
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| gluon_resnet101_v1d | 80.424 (19.576) | 95.020 (4.980) | 44.57 | bicubic | 224 |
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| tf_efficientnet_b2_ap *tfp | 80.420 (19.580) | 95.040 (4.960) | 9.11 | bicubic | 260 |
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| gluon_resnext101_32x4d | 80.334 (19.666) | 94.926 (5.074) | 44.18 | bicubic | 224 |
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| tf_efficientnet_b2_ap | 80.306 (19.694) | 95.028 (4.972) | 9.11 | bicubic | 260 |
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| gluon_resnet101_v1s | 80.300 (19.700) | 95.150 (4.850) | 44.67 | bicubic | 224 |
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| tf_efficientnet_b2 *tfp | 80.188 (19.812) | 94.974 (5.026) | 9.11 | bicubic | 260 |
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| tf_efficientnet_b2 | 80.086 (19.914) | 94.908 (5.092) | 9.11 | bicubic | 260 |
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| gluon_resnet152_v1c | 79.916 (20.084) | 94.842 (5.158) | 60.21 | bicubic | 224 |
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| gluon_seresnext50_32x4d | 79.912 (20.088) | 94.818 (5.182) | 27.56 | bicubic | 224 |
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| gluon_resnet152_v1b | 79.692 (20.308) | 94.738 (5.262) | 60.19 | bicubic | 224 |
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| gluon_xception65 | 79.604 (20.396) | 94.748 (5.252) | 39.92 | bicubic | 299 |
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| gluon_resnet101_v1c | 79.544 (20.456) | 94.586 (5.414) | 44.57 | bicubic | 224 |
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| tf_efficientnet_b1_ap *tfp | 79.532 (20.468) | 94.378 (5.622) | 7.79 | bicubic | 240 |
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| tf_efficientnet_cc_b1_8e *tfp | 79.464 (20.536)| 94.492 (5.508) | 39.7 | bicubic | 240 |
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| gluon_resnext50_32x4d | 79.356 (20.644) | 94.424 (5.576) | 25.03 | bicubic | 224 |
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| gluon_resnet101_v1b | 79.304 (20.696) | 94.524 (5.476) | 44.55 | bicubic | 224 |
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| tf_efficientnet_cc_b1_8e | 79.298 (20.702) | 94.364 (5.636) | 39.7 | bicubic | 240 |
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| tf_efficientnet_b1_ap | 79.278 (20.722) | 94.308 (5.692) | 7.79 | bicubic | 240 |
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| tf_efficientnet_b1 *tfp | 79.172 (20.828) | 94.450 (5.550) | 7.79 | bicubic | 240 |
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| gluon_resnet50_v1d | 79.074 (20.926) | 94.476 (5.524) | 25.58 | bicubic | 224 |
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| tf_efficientnet_em *tfp | 78.958 (21.042) | 94.458 (5.542) | 6.90 | bicubic | 240 |
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| tf_mixnet_l *tfp | 78.846 (21.154) | 94.212 (5.788) | 7.33 | bilinear | 224 |
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| tf_efficientnet_b1 | 78.826 (21.174) | 94.198 (5.802) | 7.79 | bicubic | 240 |
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| gluon_inception_v3 | 78.804 (21.196) | 94.380 (5.620) | 27.16M | bicubic | 299 |
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| tf_mixnet_l | 78.770 (21.230) | 94.004 (5.996) | 7.33 | bicubic | 224 |
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| tf_efficientnet_em | 78.742 (21.258) | 94.332 (5.668) | 6.90 | bicubic | 240 |
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| gluon_resnet50_v1s | 78.712 (21.288) | 94.242 (5.758) | 25.68 | bicubic | 224 |
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| tf_efficientnet_cc_b0_8e *tfp | 78.314 (21.686) | 93.790 (6.210) | 24.0 | bicubic | 224 |
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| gluon_resnet50_v1c | 78.010 (21.990) | 93.988 (6.012) | 25.58 | bicubic | 224 |
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| tf_efficientnet_cc_b0_8e | 77.908 (22.092) | 93.656 (6.344) | 24.0 | bicubic | 224 |
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| tf_inception_v3 | 77.856 (22.144) | 93.644 (6.356) | 27.16M | bicubic | 299 |
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| tf_efficientnet_cc_b0_4e *tfp | 77.746 (22.254) | 93.552 (6.448) | 13.3 | bicubic | 224 |
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| tf_efficientnet_es *tfp | 77.616 (22.384) | 93.750 (6.250) | 5.44 | bicubic | 224 |
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| gluon_resnet50_v1b | 77.578 (22.422) | 93.718 (6.282) | 25.56 | bicubic | 224 |
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| adv_inception_v3 | 77.576 (22.424) | 93.724 (6.276) | 27.16M | bicubic | 299 |
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| tf_efficientnet_b0_ap *tfp | 77.514 (22.486) | 93.576 (6.424) | 5.29 | bicubic | 224 |
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| tf_efficientnet_cc_b0_4e | 77.304 (22.696) | 93.332 (6.668) | 13.3 | bicubic | 224 |
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| tf_efficientnet_es | 77.264 (22.736) | 93.600 (6.400) | 5.44 | bicubic | 224 |
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| tf_efficientnet_b0 *tfp | 77.258 (22.742) | 93.478 (6.522) | 5.29 | bicubic | 224 |
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| tf_efficientnet_b0_ap | 77.084 (22.916) | 93.254 (6.746) | 5.29 | bicubic | 224 |
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| tf_mixnet_m *tfp | 77.072 (22.928) | 93.368 (6.632) | 5.01 | bilinear | 224 |
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| tf_mixnet_m | 76.950 (23.050) | 93.156 (6.844) | 5.01 | bicubic | 224 |
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| tf_efficientnet_b0 | 76.848 (23.152) | 93.228 (6.772) | 5.29 | bicubic | 224 |
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| tf_mixnet_s *tfp | 75.800 (24.200) | 92.788 (7.212) | 4.13 | bilinear | 224 |
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| tf_mobilenetv3_large_100 *tfp | 75.768 (24.232) | 92.710 (7.290) | 5.48 | bilinear | 224 |
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| tf_mixnet_s | 75.648 (24.352) | 92.636 (7.364) | 4.13 | bicubic | 224 |
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| tf_mobilenetv3_large_100 | 75.516 (24.484) | 92.600 (7.400) | 5.48 | bilinear | 224 |
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| gluon_resnet34_v1b | 74.580 (25.420) | 91.988 (8.012) | 21.80 | bicubic | 224 |
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| tf_mobilenetv3_large_075 *tfp | 73.730 (26.270) | 91.616 (8.384) | 3.99 | bilinear | 224 |
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| tf_mobilenetv3_large_075 | 73.442 (26.558) | 91.352 (8.648) | 3.99 | bilinear | 224 |
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| tf_mobilenetv3_large_minimal_100 *tfp | 72.678 (27.322) | 90.860 (9.140) | 3.92 | bilinear | 224 |
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| tf_mobilenetv3_large_minimal_100 | 72.244 (27.756) | 90.636 (9.364) | 3.92 | bilinear | 224 |
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| tf_mobilenetv3_small_100 *tfp | 67.918 (32.082) | 87.958 (12.042 | 2.54 | bilinear | 224 |
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| tf_mobilenetv3_small_100 | 67.918 (32.082) | 87.662 (12.338) | 2.54 | bilinear | 224 |
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| tf_mobilenetv3_small_075 *tfp | 66.142 (33.858) | 86.498 (13.502) | 2.04 | bilinear | 224 |
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| tf_mobilenetv3_small_075 | 65.718 (34.282) | 86.136 (13.864) | 2.04 | bilinear | 224 |
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| tf_mobilenetv3_small_minimal_100 *tfp | 63.378 (36.622) | 84.802 (15.198) | 2.04 | bilinear | 224 |
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| tf_mobilenetv3_small_minimal_100 | 62.898 (37.102) | 84.230 (15.770) | 2.04 | bilinear | 224 |
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Models with `*tfp` next to them were scored with `--tf-preprocessing` flag.
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The `tf_efficientnet`, `tf_mixnet` models require an equivalent for 'SAME' padding as their arch results in asymmetric padding. I've added this in the model creation wrapper, but it does come with a performance penalty.
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Sources for original weights:
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* `tf_efficientnet*`: [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet)
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* `tf_efficientnet_e*`: [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu)
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* `tf_mixnet*`: [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet)
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* `tf_inception*`: [Tensorflow Slim](https://github.com/tensorflow/models/tree/master/research/slim)
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* `gluon_*`: [MxNet Gluon](https://gluon-cv.mxnet.io/model_zoo/classification.html)
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## Usage
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### Environment
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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.
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PyTorch versions 1.0 and 1.1 have been tested with this code.
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I've tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda:
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```
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conda create -n torch-env
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conda activate torch-env
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conda install -c pytorch pytorch torchvision cudatoolkit=10.0
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```
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### Pip
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This package can be installed via pip. Currently, the model factory (`timm.create_model`) is the most useful component to use via a pip install.
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Install (after conda env/install):
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```
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pip install timm
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```
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Use:
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```
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>>> import timm
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>>> m = timm.create_model('mobilenetv3_100', pretrained=True)
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>>> m.eval()
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```
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### Scripts
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A train, validation, inference, and checkpoint cleaning script included in the github root folder. Scripts are not currently packaged in the pip release.
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#### Training
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The variety of training args is large and not all combinations of options (or even options) have been fully tested. For the training dataset folder, specify the folder to the base that contains a `train` and `validation` folder.
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To train an SE-ResNet34 on ImageNet, locally distributed, 4 GPUs, one process per GPU w/ cosine schedule, random-erasing prob of 50% and per-pixel random value:
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`./distributed_train.sh 4 /data/imagenet --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 -j 4`
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NOTE: NVIDIA APEX should be installed to run in per-process distributed via DDP or to enable AMP mixed precision with the --amp flag
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#### Validation / Inference
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Validation and inference scripts are similar in usage. One outputs metrics on a validation set and the other outputs topk class ids in a csv. Specify the folder containing validation images, not the base as in training script.
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To validate with the model's pretrained weights (if they exist):
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`python validate.py /imagenet/validation/ --model seresnext26_32x4d --pretrained`
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To run inference from a checkpoint:
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`python inference.py /imagenet/validation/ --model mobilenetv3_100 --checkpoint ./output/model_best.pth.tar`
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## TODO
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|
A number of additions planned in the future for various projects, incl
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* Do a model performance (speed + accuracy) benchmarking across all models (make runable as script)
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* Add usage examples to comments, good hyper params for training
|
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* Comments, cleanup and the usual things that get pushed back
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