数据集（benchmark）、常用数据集的解析（cifar-10、）

What is the class of this image ?

主要是以下常见的数据集，用以衡量算法的分类准确率：

• mnist、cifar-10、cifar-100stl-10
• svhn、ILSVRC2012 task 1

1. cifar-10

CIFAR-10 and CIFAR-100 datasets

• cifar-10-batches-py（Python 接口）

  import os
  import pickle
  import numpy as np
  
  def load_CIFAR10_batch(filename):
      with open(filename, 'rb') as f:
          data = pickle.load(f, encoding='latin1')
          X = data['data']
          y = data['labels']
          X = X.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1).astype(np.float32)
          y = np.array(y)
          return X, y
  
  def load_CIFAR10(root):
      xs, ys = [], []
      for n in range(1, 6):
          filename = os.path.join(root, 'data_batch_{}'.format(n))
          X, y = load_CIFAR10_batch(filename)
          xs.append(X)
          ys.append(y)
      Xtr = np.concatenate(xs)
      Ytr = np.concatenate(ys)
      Xte, Yte = load_CIFAR10_batch(os.path.join(root, 'test_batch'))
      return Xtr, Ytr, Xte, Yte

  对于描述数据信息的信息（batches.meta），仍然可以使用 pickle.load 的形式加载，加载的结果仍然是一个字典类型：

  with open('batches.meta', 'rb') as f:
      data = pickle.load(f, encoding='latin1')
  print(data)
  
  {'label_names': ['airplane',
    'automobile',
    'bird',
    'cat',
    'deer',
    'dog',
    'frog',
    'horse',
    'ship',
    'truck'],
   'num_cases_per_batch': 10000,
   'num_vis': 3072}

• cifar-10-batches-mat（matlab 接口）

  最方便的方式是调用 matlab 内置已封装好的 api，helperCIFAR10Data.download/load，或者使用 edit helperCIFAR10Data查看其实现；

  function [train_x, train_y, test_x, test_y] = load_cifar(filepath)
  
      train_x = []; train_y = [];
      for i = 1:5
          filename = fullfile(filepath, sprintf('data_batch_%d.mat', i));
          [batch_train, batch_labels] = load_batch_as_4d_tensor(filename, true);
          train_x = cat(4, train_x, batch_train);
          train_y = [train_y; batch_labels];
      end
      filename = fullfile(filepath, 'test_batch.mat');
      [test_x, test_y] = load_batch_as_4d_tensor(filename, true);
  end
  
  function [train_x, train_y] = load_batch_as_4d_tensor(filename, to_categorical)
  % 这里的 x_train 是 4 维的 tensor, 32*32*3*num
      if  ~exist('to_categorical', 'var') || isempty(to_categorical)
          to_categorical = false;
      end
      load(filename);
      train_x = reshape(data', 32, 32, 3, []);
      train_x = permute(train_x, [2, 1, 3, 4]);       % 互换第一维和第二维
      train_y = labels;
      if to_categorical
          metafile = fullfile(fileparts(filename), 'batches.meta.mat');
          load(metafile);
          train_y = categorical(train_y, 0:9, label_names);
      end
  
  end