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_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
    pretrained='torchvision://resnet101',
    backbone=dict(type='ResNet', depth=101))

data = dict(
    test=dict(
        type='CustomDataset',
        img_dir='/mnt/nfs-mnj-hot-01/tmp/msaito/ddad/train_val/GT/000000/rgb/CAMERA_09',
        img_suffix='.png',

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import gzip
import os

import numpy as np
import six
from six.moves.urllib import request

parent = 'http://yann.lecun.com/exdb/mnist'
train_images = 'train-images-idx3-ubyte.gz'
train_labels = 'train-labels-idx1-ubyte.gz'

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Caffe tutorial

この文章ではCNN実装であるCaffeを用いて，特徴ベクトルの抽出やパラメータの学習を行うための方法について説明する．

Caffeでサポートされている機能

以下の作業を行いたいのであれば，Caffeを用いることが望ましい．

• CNNを利用した画像の多クラス分類
• CNNによる特徴ベクトルの抽出
• CNNの転移学習
• Stacked Auto Encoder

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#pragma once

#include <boost/cstdint.hpp>
#include <boost/config.hpp>
#include <cmath>

namespace aoba {

struct halton_sequence_engine {
public:

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#pragma once

#include <algorithm>
#include <functional>
#include <boost/iterator/iterator_facade.hpp>
#include <boost/tuple/tuple.hpp>

namespace aoba {
namespace detail {

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/*
 * Copyright (c) 2012 Masaki Saito
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *