在哪里下载idx3-ubyte格式的fashionMNIST数据集
时间: 2024-05-18 20:17:44 浏览: 12
Fashion-MNIST数据集的官方网站是:https://github.com/zalandoresearch/fashion-mnist,你可以在该网站上下载包含idx3-ubyte格式的数据集文件。
具体的下载步骤如下:
1. 打开Fashion-MNIST官方网站:https://github.com/zalandoresearch/fashion-mnist
2. 在网站页面中,找到“Download (gzip)”这一行,并点击其中的链接:https://github.com/zalandoresearch/fashion-mnist/blob/master/data/fashion/t10k-images-idx3-ubyte.gz
3. 下载完成后,你会得到一个名为“t10k-images-idx3-ubyte.gz”的压缩文件。
4. 解压该压缩文件,你会得到一个名为“t10k-images-idx3-ubyte”的文件,这就是Fashion-MNIST数据集中测试集的图像数据文件。
5. 你可以重复上述步骤,下载包含训练集和标签数据的数据集文件。
需要注意的是,idx3-ubyte格式是一种二进制格式,如果你想要查看其中的内容,需要使用特定的工具进行解析。在Python中,可以使用numpy库中的load函数进行解析。
相关问题
matlab如何获取png格式的fashion mnist数据集
可以通过以下步骤在Matlab中获取Fashion MNIST数据集:
1. 在Matlab中打开命令窗口,输入以下命令下载Fashion MNIST数据集:
```
urlwrite('https://github.com/zalandoresearch/fashion-mnist/raw/master/data/fashion/t10k-images-idx3-ubyte.gz','t10k-images-idx3-ubyte.gz');
urlwrite('https://github.com/zalandoresearch/fashion-mnist/raw/master/data/fashion/t10k-labels-idx1-ubyte.gz','t10k-labels-idx1-ubyte.gz');
urlwrite('https://github.com/zalandoresearch/fashion-mnist/raw/master/data/fashion/train-images-idx3-ubyte.gz','train-images-idx3-ubyte.gz');
urlwrite('https://github.com/zalandoresearch/fashion-mnist/raw/master/data/fashion/train-labels-idx1-ubyte.gz','train-labels-idx1-ubyte.gz');
```
2. 解压缩下载的文件,可以使用以下命令:
```
gunzip t10k-images-idx3-ubyte.gz
gunzip t10k-labels-idx1-ubyte.gz
gunzip train-images-idx3-ubyte.gz
gunzip train-labels-idx1-ubyte.gz
```
3. 加载图片和标签数据:
```
test_images = processImages('t10k-images-idx3-ubyte');
test_labels = processLabels('t10k-labels-idx1-ubyte');
train_images = processImages('train-images-idx3-ubyte');
train_labels = processLabels('train-labels-idx1-ubyte');
```
其中,`processImages`和`processLabels`是两个自定义函数,用于解析数据集文件并返回图像和标签数据。这两个函数的代码如下:
```
function images = processImages(filename)
fid = fopen(filename,'r','b');
if fid == -1
error('Error: Cannot open file %s',filename);
end
magicNumber = fread(fid,1,'int32',0,'b');
numImages = fread(fid,1,'int32',0,'b');
numRows = fread(fid,1,'int32',0,'b');
numCols = fread(fid,1,'int32',0,'b');
images = fread(fid,inf,'unsigned char');
images = reshape(images,numCols,numRows,numImages);
images = permute(images,[2 1 3]);
fclose(fid);
end
function labels = processLabels(filename)
fid = fopen(filename,'r','b');
if fid == -1
error('Error: Cannot open file %s',filename);
end
magicNumber = fread(fid,1,'int32',0,'b');
numItems = fread(fid,1,'int32',0,'b');
labels = fread(fid,inf,'unsigned char');
fclose(fid);
end
```
4. 将图像数据转换为PNG格式:
```
for i = 1:size(test_images,3)
imwrite(test_images(:,:,i),sprintf('test_%d.png',i));
end
for i = 1:size(train_images,3)
imwrite(train_images(:,:,i),sprintf('train_%d.png',i));
end
```
这将把每个图像保存为一个PNG格式的文件。
Fashion-MNIST数据集的下载与读取
Fashion-MNIST数据集是一个用于图像分类任务的数据集,包含了10个类别的70,000张28x28的灰度图像。下面是下载和读取Fashion-MNIST数据集的示例代码:
下载数据集:
```python
import urllib.request
import os
url_train = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz'
url_train_label = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz'
url_test = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz'
url_test_label = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz'
os.makedirs('./data/fashion_mnist', exist_ok=True)
urllib.request.urlretrieve(url_train, './data/fashion_mnist/train-images-idx3-ubyte.gz')
urllib.request.urlretrieve(url_train_label, './data/fashion_mnist/train-labels-idx1-ubyte.gz')
urllib.request.urlretrieve(url_test, './data/fashion_mnist/t10k-images-idx3-ubyte.gz')
urllib.request.urlretrieve(url_test_label, './data/fashion_mnist/t10k-labels-idx1-ubyte.gz')
```
读取数据集:
```python
import gzip
import numpy as np
def load_mnist_images(filename):
with gzip.open(filename, 'rb') as f:
data = np.frombuffer(f.read(), np.uint8, offset=16)
return data.reshape(-1, 28, 28)
def load_mnist_labels(filename):
with gzip.open(filename, 'rb') as f:
data = np.frombuffer(f.read(), np.uint8, offset=8)
return data
train_images = load_mnist_images('./data/fashion_mnist/train-images-idx3-ubyte.gz')
train_labels = load_mnist_labels('./data/fashion_mnist/train-labels-idx1-ubyte.gz')
test_images = load_mnist_images('./data/fashion_mnist/t10k-images-idx3-ubyte.gz')
test_labels = load_mnist_labels('./data/fashion_mnist/t10k-labels-idx1-ubyte.gz')
```
这里的`load_mnist_images`和`load_mnist_labels`函数用于读取数据集文件,并将其转换为NumPy数组。`train_images`和`test_images`是形状为`(60000, 28, 28)`和`(10000, 28, 28)`的数组,表示训练集和测试集的图像数据,每张图像的大小为28x28。`train_labels`和`test_labels`是形状为`(60000,)`和`(10000,)`的数组,表示训练集和测试集的标签数据。