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ResNet variations for TensorFlow.

Project description

TensorFlow-ResNets

PyPI version

This repository contains TensorFlow Keras ResNet models. Below, you will find the supported variants of ResNet and what weights are supported.

The codebase takes inspiration from TensorFlow ResNets and PyTorch ResNets.

This repository is compatible with TF 2.2.0 even though grouped convolutions are only supported in TF Nightly. Grouped convolutions will be imported from convolutions.py which is the TF Nightly version of convolution layers. This allows the use of TPUs since they don't work with TF Nightly.

These models will not work without a GPU or TPU due to the use of grouped convolutions.

Installation


To install, you can use pip install tf2-resnets.
To use a model, you can do

from tf2_resnets import models

# Weights here are ImageNet.
# They can also be 'ssl' (semi-supervised)
# or 'swsl' (semi-weakly supervised)
# for a selection of models.
model = models.ResNeXt50(input_shape=(224, 224, 3), weights='imagenet')

Models


Architecture Weights Top-1 Acc. Top-5 Acc.
ResNet-18 ImageNet 69.8 89.1
ResNet-34 ImageNet 73.3 91.4
ResNet-50 ImageNet 76.2 92.9
ResNet-101 ImageNet 77.4 93.6
ResNet-152 ImageNet 78.3 94.1
ResNeXt-50 32x4d ImageNet 77.6 93.7
ResNeXt-101 32x8d ImageNet 79.3 94.5
Wide ResNet-50 2 ImageNet 78.5 94.1
Wide ResNet-101 2 ImageNet 78.8 94.3
ResNeSt-50 ImageNet 81.0* N/A
ResNeSt-101 ImageNet 82.8* N/A
ResNeSt-200 ImageNet 83.8* N/A
ResNeSt-269 ImageNet 84.5* N/A
ResNet-18 semi-supervised 72.8 91.5
ResNet-50 semi-supervised 79.3 94.9
ResNet-18 semi-weakly supervised 73.4 91.9
ResNet-50 semi-weakly supervised 81.2 96.0
ResNeXt-50 32x4d semi-supervised 80.3 95.4
ResNeXt-101 32x8d semi-supervised 81.7 96.1
ResNeXt-50 32x4d semi-weakly supervised 82.2 96.3
ResNeXt-101 32x8d semi-weakly supervised 84.3 97.2

* ResNeSt models' Top-1 Accuracies were reported using different crop sizes. Crop sizes in order - 224, 256, 320, and 416.

Preprocessing


There are two different preprocessing functions which are meant to be used according to the task at hand. The first one, located in imagenet_preprocessing.py, is meant to be used for reproducing the ImageNet results. The second function is located inside resnet.py. This function is meant to be used for transfer learning. The first function includes an additional resize to 256 by 256 and a central crop to 224 by 224.

Original Implementations


The original implementions of these models are listed below.

Project details


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