The Benchmarking Keras PyTorch GitHub project benchmarks every pre-trained model in PyTorch and Keras (Tensorflow). All benchmarks are reproducible.

GitHub Link: https://github.com/cgnorthcutt/benchmarking-keras-pytorch

Why this is helpful

Combining Keras and PyTorch benchmarks into a single framework lets researchers decide which platform is best for a given model. For example resnet architectures perform better in PyTorch and inception architectures perform better in Keras (see below). These benchmarks serve as a standard from which to start new projects or debug current implementations.

But pre-trained models are already reproducible… right?

In PyTorch, yes. However some Keras users struggle with reproducibility, with issues falling into three categories:

  1. The published benchmarks on Keras Applications cannot be reproduced, even when exactly copying the example code. In fact, their reported accuracies (as of Feb. 2019) are usually higher than the actual accuracies. See here1 and here2.
  2. Some pre-trained Keras models yield inconsistent or lower accuracies when deployed on a server (here3) or run in sequence with other Keras models (here4).
  3. Keras models using batch normalization can be unreliable. For some models, forward-pass evaluations (with gradients supposedly off) still result in weights changing at inference time. See here5.

One of the goals of this project is to help reconcile some of these issues with reproducible benchmarks for Keras pre-trained models. The way I deal with these issues is three-fold. In Keras I

  1. avoid batches during inference.
  2. run each example one at a time. This is silly slow, but yields a reproducible output for every model.
  3. only run models in local functions or use with clauses to ensure no aspects of a previous model persist in memory when the next model is loaded.

Below I provide a table of actual validation set accuracies for both Keras and PyTorch (verified on macOS 10.11.6, Linux Debian 9, and Ubuntu 18.04).

Benchmark Results on ImageNet

Platform Model Acc@1 Acc@5 Rank@1 Rank@5
Keras 2.2.4 nasnetlarge 80.83 95.27 1 1
Keras 2.2.4 inceptionresnetv2 78.93 94.45 2 2
PyTorch 1.0 resnet152 77.62 93.81 3 3
Keras 2.2.4 xception 77.18 93.49 4 4
PyTorch 1.0 densenet161 76.92 93.49 5 5
PyTorch 1.0 resnet101 76.64 93.30 6 6
PyTorch 1.0 densenet201 76.41 93.18 7 7
Keras 2.2.4 inceptionv3 76.02 92.90 8 8
PyTorch 1.0 densenet169 75.59 92.69 9 9
PyTorch 1.0 resnet50 75.06 92.48 10 10
Keras 2.2.4 densenet201 74.77 92.32 11 11
PyTorch 1.0 densenet121 74.07 91.94 12 12
Keras 2.2.4 densenet169 73.92 91.76 13 13
PyTorch 1.0 vgg19_bn 72.90 91.35 14 14
PyTorch 1.0 resnet34 72.34 90.84 15 16
PyTorch 1.0 vgg16_bn 72.29 91.01 16 15
Keras 2.2.4 densenet121 72.09 90.70 17 17
Keras 2.2.4 nasnetmobile 71.59 90.19 18 19
PyTorch 1.0 vgg19 71.19 90.40 19 18
PyTorch 1.0 vgg16 70.66 89.93 20 20
Keras 2.2.4 resnet50 70.35 89.55 21 22
PyTorch 1.0 vgg13_bn 70.12 89.56 22 21
Keras 2.2.4 mobilenetV2 69.98 89.49 23 23
PyTorch 1.0 vgg11_bn 69.36 89.06 24 24
PyTorch 1.0 inception_v3 69.25 88.69 25 25
Keras 2.2.4 mobilenet 69.02 88.48 26 27
PyTorch 1.0 vgg13 68.69 88.65 27 28
PyTorch 1.0 resnet18 68.37 88.56 28 26
PyTorch 1.0 vgg11 67.85 88.11 29 29
Keras 2.2.4 vgg19 65.58 86.54 30 30
Keras 2.2.4 vgg16 65.24 86.20 31 31
PyTorch 1.0 squeezenet1_0 56.49 79.06 32 33
PyTorch 1.0 squeezenet1_1 56.42 79.21 33 32
PyTorch 1.0 alexnet 54.50 77.64 34 34

To Reproduce

Get the ImageNet validation dataset

  • Download
  • Preprocess/Extract validation data
    • Once ILSVRC2012_img_val.tar is downloaded, run:
      # Credit to Soumith: https://github.com/soumith/imagenet-multiGPU.torch
      $ cd ../ && mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar
      $ wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash
      

Reproduce in 10 seconds

The top 5 predictions for every example in the ImageNet validation set have been pre-computed for you here for Keras models and here for PyTorch models. The following code will use this for you to produce Keras and PyTorch benchmarking in a few seconds:

$ git clone https://github.com:cgnorthcutt/imagenet-benchmarking.git
$ cd benchmarking-keras-pytorch
$ python imagenet_benchmarking.py /path/to/imagenet_val_data

Reproduce model outputs (hours)

You can also reproduce the inference-time output of each Keras and PyTorch model without using the pre-computed data. Inference for Keras takes a long time (5-10 hours) because I compute the forward pass on each example one at a time and avoid vectorized operations: this was the only approach I found would reliably reproduce the same accuracies. PyTorch is fairly quick (less than one hour). To reproduce:

$ git clone https://github.com:cgnorthcutt/imagenet-benchmarking.git
$ cd benchmarking-keras-pytorch
$ # Compute outputs of PyTorch models (1 hour)
$ ./imagenet_pytorch_get_predictions.py /path/to/imagenet_val_data
$ # Compute outputs of Keras models (5-10 hours)
$ ./imagenet_keras_get_predictions.py /path/to/imagenet_val_data
$ # View benchmark results
$ ./imagenet_benchmarking.py /path/to/imagenet_val_data

You can control GPU usage, batch size, output storage directories, and more. Run the files with the -h flag to see command line argument options.

Acknowledgments

Thanks to Jessy Lin for pointing out the issues with batch normalization in Keras and Anish Athalye for feedback.

Disclaimer: Did you find a mistake? I probably made plenty. Help make this article better by commenting below!