Fashion MNIST

### Context

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does work on MNIST, it may still fail on others."

Zalando seeks to replace the original MNIST dataset

### Content

Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. The training and test data sets have 785 columns. The first column consists of the class labels (see above), and represents the article of clothing. The rest of the columns contain the pixel-values of the associated image.

- To locate a pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27. The pixel is located on row i and column j of a 28 x 28 matrix.
- For example, pixel31 indicates the pixel that is in the fourth column from the left, and the second row from the top, as in the ascii-diagram below.
<br><br>

**Labels**

Each training and test example is assigned to one of the following labels:

- 0 T-shirt/top
- 1 Trouser
- 2 Pullover
- 3 Dress
- 4 Coat
- 5 Sandal
- 6 Shirt
- 7 Sneaker
- 8 Bag
- 9 Ankle boot
<br><br>

TL;DR

- Each row is a separate image
- Column 1 is the class label.
- Remaining columns are pixel numbers (784 total).
- Each value is the darkness of the pixel (1 to 255)

### Acknowledgements

- Original dataset was downloaded from [https://github.com/zalandoresearch/fashion-mnist][1]

- Dataset was converted to CSV with this script: [https://pjreddie.com/projects/mnist-in-csv/][2]

### License

The MIT License (MIT) Copyright © [2017] Zalando SE, https://tech.zalando.com

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:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

[1]: https://github.com/zalandoresearch/fashion-mnist
[2]: https://pjreddie.com/projects/mnist-in-csv/

### 背景信息
Fashion-MNIST为Zalando公司的商品图像数据集，包含60,000个训练样本和10,000个测试样本。每个样本为28x28的灰度图像，并关联10个类别中的一个标签。Zalando旨在使Fashion-MNIST成为原始MNIST数据集的直接替代品，用于基准测试机器学习算法。它与原始MNIST数据集具有相同的图像尺寸和训练/测试分割结构。

原始MNIST数据集包含大量的手写数字。AI/ML/Data科学社区的成员对这一数据集情有独钟，并将其用作验证算法的基准。实际上，MNIST往往是研究人员首先尝试的数据集。‘如果它不能在MNIST上运行，那么它将无法在任何地方运行’，他们如是说。‘好吧，如果它能在MNIST上运行，那么它仍然可能在其他地方失败’。

Zalando寻求替换原始的MNIST数据集。

### 数据内容
每个图像高度和宽度均为28像素，总计784像素。每个像素与一个单像素值相关联，表示该像素的明暗程度，数值越高表示越暗。该像素值是一个介于0到255之间的整数。训练集和测试集共有785列。第一列包含类别标签（见上文），代表服装类别。其余列包含相关图像的像素值。

- 为了定位图像中的像素，假设我们将x分解为x = i * 28 + j，其中i和j是介于0到27之间的整数。该像素位于28x28矩阵的第i行第j列。

- 例如，像素31表示位于左数第四列、顶部第二行的像素，如以下ascii图所示。

**标签

每个训练样本和测试样本被分配到以下标签之一：

- 0：T恤/上衣
- 1：裤子
- 2：套头衫
- 3：连衣裙
- 4：外套
- 5：凉鞋
- 6：衬衫
- 7：运动鞋
- 8：包
- 9：踝靴

TL;DR
- 每行代表一个单独的图像
- 第一列是类别标签。
- 剩余列是像素数值（总计784个）。
- 每个值代表像素的暗度（1至255）。

### 致谢
- 原始数据集从[https://github.com/zalandoresearch/fashion-mnist][1]下载。
- 数据集通过此脚本转换为CSV格式：[https://pjreddie.com/projects/mnist-in-csv/][2]。

### 许可
MIT许可（MIT）版权所有 © [2017] Zalando SE，https://tech.zalando.com

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[1]: https://github.com/zalandoresearch/fashion-mnist
[2]: https://pjreddie.com/projects/mnist-in-csv/