diff --git a/R/TaskClassif_tiny_imagenet.R b/R/TaskClassif_tiny_imagenet.R
index fbd27f9e..e1c0e744 100644
--- a/R/TaskClassif_tiny_imagenet.R
+++ b/R/TaskClassif_tiny_imagenet.R
@@ -31,7 +31,7 @@ NULL
 #   The cache_dir/datasets/tiny_imagenet folder.
 constructor_tiny_imagenet = function(path) {
   # path points to {cache_dir, tempfile}/data/tiny_imagenet
-  torchvision::tiny_imagenet_dataset(root = file.path(path ), download = TRUE)
+  torchvision::tiny_imagenet_dataset(root = file.path(path), download = TRUE)
   download_folder = file.path(path, "tiny-imagenet-200")
 
   lookup = fread(sprintf("%s/words.txt", download_folder), header = FALSE)
diff --git a/vignettes/articles/image_classification.Rmd b/vignettes/articles/image_classification.Rmd
new file mode 100644
index 00000000..ec30a59a
--- /dev/null
+++ b/vignettes/articles/image_classification.Rmd
@@ -0,0 +1,52 @@
+---
+title: "Image Classification"
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+In the *Get Started* vignette, we have already explained how to train a simple neural network on tabular data.
+In this article you will learn how to work with image data.
+For that `mlr3torch` relies on the functionality provided by the [`torchvision`](https://github.com/mlverse/torchvision) package.
+As an example task, we will use the "tiny imagenet" dataset, which is a subset of the [ImageNet](http://www.image-net.org/) dataset.
+It consists of 200 classes with 500 training images each.
+The goal is to predict the class of an image from the pixels.
+
+```{r setup}
+set.seed(314)
+library(mlr3torch)
+
+task = tsk("tiny_imagenet")
+task
+```
+
+In `mlr3torch`, image data is represented using the `mlr3torch::imageuri` class.
+It is essentially a character vector, containing paths to images on the file system.
+Note that this task has to be downloaded from the internet when accessing the data.
+In order to download the dataset only once, you must set the `mlr3torch.cache` option to `TRUE`, or a specific path to be used as the cache folder.
+
+```{r, eval = FALSE}
+options(mlr3torch.cache = TRUE)
+```
+
+We can e.g. print the path to the first image as follows:
+
+```{r}
+task$data(1, "image")
+```
+
+
+As a learner, we we will use the famous AlexNet classification network, which sparked the "Deep Learning revolution" in 2012.
+
+
+```{r}
+alexnet = lrn("classif.alexnet")
+alexnet
+```
+
+
+