jl1990

# -*- coding: utf-8 -*-

import cv2
import numpy as np
import copy

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Flatten, Activation, add
from keras.optimizers import SGD
from keras.layers.normalization import BatchNormalization
from keras.models import Model

jl1990

ResNet-152 in Keras

This is an Keras implementation of ResNet-152 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.

ResNet Paper:

Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385

jl1990

public interface Step<I, O>
{
    O execute(I value);

    default <A> Step<I, A> pipe(Step<O, A> source) {
        return input -> source.execute(execute(input));
    }

    static <I, O> Step<I, O> of(Step<I, O> source) {
        return source;