Update image-classification-dataset.md

chapter_linear-classification/image-classification-dataset.md

@@ -128,9 +128,14 @@ data = FashionMNIST(resize=(32, 32))
 len(data.train[0]), len(data.val[0])
 ```
 
-The images are grayscale and upscaled to $32 \times 32$ pixels in resolution above. This is similar to the original MNIST dataset which consisted of (binary) black and white images. Note, though, that most modern image data has three channels (red, green, blue) and that hyperspectral images can have in excess of 100 channels (the HyMap sensor has 126 channels).
+
+After running this cell, you should see that 4 .gz files (2 starts with t10k refers to testing data and 2 starts with train refers to training data) are downloaded into folder d2l/data/FashionMNIST/ and 4 corresponding .idx3-ubyte files are created in folder d2l/data/FashionMNIST/raw/. This is because torchvision.datasets.FashionMNIST automatically download the gzip-compressed files such as t10k-images-idx3-ubyte.gz  and then extract them into t10k-images-idx3-ubyte. The uncompressed file is in the IDX format, which is a file format commonly used for storing and distributing binary data, particularly for datasets in the machine learning and computer vision domains. This is why it doesn't look like a folder with images and labels for your to open and view.
+
+The images are grayscale and upscaled to $32 \times 32$ pixels in resolution above. This is similar to the original MNIST dataset which consisted of (binary) black and white images. Note, though, that most modern image data which has 3 channels (red, green, blue) and hyperspectral images which can have in excess of 100 channels (the HyMap sensor has 126 channels).
+By convention we store image as a $c \times h \times w$ tensor, where $c$ is the number of color channels, $h$ is the height and $w$ is the width. The images are grayscale and upscaled to $32 \times 32$ pixels in resolution above. This is similar to the original MNIST dataset which consisted of (binary) black and white images. Note, though, that most modern image data has three channels (red, green, blue) and that hyperspectral images can have in excess of 100 channels (the HyMap sensor has 126 channels).
 By convention we store an image as a $c \times h \times w$ tensor, where $c$ is the number of color channels, $h$ is the height and $w$ is the width.
 
+
 ```{.python .input}
 %%tab all
 data.train[0][0].shape