From 48a64d3b8afb72bfb8f29c781f8d4926736ccbb3 Mon Sep 17 00:00:00 2001
From: Guillaume Lagrange <lagrange.guillaume.1@gmail.com>
Date: Thu, 22 Aug 2024 15:47:05 -0400
Subject: [PATCH] Add images and csv dataset source to book (#2179)

---
 burn-book/src/building-blocks/dataset.md | 51 +++++++++++++++++++++++-
 1 file changed, 50 insertions(+), 1 deletion(-)

diff --git a/burn-book/src/building-blocks/dataset.md b/burn-book/src/building-blocks/dataset.md
index 70ba3bf27..f54fb93eb 100644
--- a/burn-book/src/building-blocks/dataset.md
+++ b/burn-book/src/building-blocks/dataset.md
@@ -104,7 +104,7 @@ dataset to use should be based on the dataset's size as well as its intended pur
 
 ## Sources
 
-For now, there is only one dataset source available with Burn, but more to come!
+For now, there are only a couple of dataset sources available with Burn, but more to come!
 
 ### Hugging Face
 
@@ -131,6 +131,55 @@ fn main() {
 We see that items must derive `serde::Serialize`, `serde::Deserialize`, `Clone`, and `Debug`, but
 those are the only requirements.
 
+### Images
+
+`ImageFolderDataset` is a generic vision dataset used to load images from disk. It is currently
+available for multi-class and multi-label classification tasks.
+
+```rust, ignore
+// Create an image classification dataset from the root folder,
+// where images for each class are stored in their respective folder.
+//
+// For example:
+// root/dog/dog1.png
+// root/dog/dog2.png
+// ...
+// root/cat/cat1.png
+let dataset = ImageFolderDataset::new_classification("path/to/dataset/root").unwrap();
+```
+
+```rust, ignore
+// Create a multi-label image classification dataset from a list of items,
+// where each item is a tuple `(image path, labels)`, and a list of classes
+// in the dataset.
+//
+// For example:
+let items = vec![
+    ("root/dog/dog1.png", vec!["animal".to_string(), "dog".to_string()]),
+    ("root/cat/cat1.png", vec!["animal".to_string(), "cat".to_string()]),
+];
+let dataset = ImageFolderDataset::new_multilabel_classification_with_items(
+    items,
+    &["animal", "cat", "dog"],
+)
+.unwrap();
+```
+
+### Comma-Separated Values (CSV)
+
+Loading records from a simple CSV file in-memory is simple with the `InMemDataset`:
+
+```rust, ignore
+// Build dataset from csv with tab ('\t') delimiter.
+// The reader can be configured for your particular file.
+let mut rdr = csv::ReaderBuilder::new();
+let rdr = rdr.delimiter(b'\t');
+
+let dataset = InMemDataset::from_csv("path/to/csv", rdr).unwrap();
+```
+
+Note that this requires the `csv` crate.
+
 **What about streaming datasets?**
 
 There is no streaming dataset API with Burn, and this is by design! The learner struct will iterate