diff --git a/NAMESPACE b/NAMESPACE
index 9a148836..1c826167 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -48,6 +48,11 @@ S3method(item_transform_random_vertical_flip,default)
 S3method(item_transform_random_vertical_flip,image_with_bounding_box)
 S3method(item_transform_random_vertical_flip,image_with_rotated_box)
 S3method(item_transform_random_vertical_flip,image_with_segmentation_mask)
+S3method(item_transform_resize,dataset)
+S3method(item_transform_resize,default)
+S3method(item_transform_resize,image_with_bounding_box)
+S3method(item_transform_resize,image_with_rotated_box)
+S3method(item_transform_resize,image_with_segmentation_mask)
 S3method(item_transform_rotate,dataset)
 S3method(item_transform_rotate,default)
 S3method(item_transform_rotate,image_with_bounding_box)
@@ -179,6 +184,7 @@ export(item_transform_pad)
 export(item_transform_perspective)
 export(item_transform_random_horizontal_flip)
 export(item_transform_random_vertical_flip)
+export(item_transform_resize)
 export(item_transform_rotate)
 export(item_transform_vflip)
 export(kmnist_dataset)
diff --git a/NEWS.md b/NEWS.md
index df7b58a1..d7c99ad5 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -23,6 +23,7 @@
 * Added `item_transform_affine()` and `target_transform_affine()` for applying an affine transformation to a dataset item and its detection target, with support for detection and segmentation items and datasets (#367).
 * Added `item_transform_center_crop()` for cropping images from the center to a specified size for dataset items, with support for detection and segmentation item types and datasets  (@DerrickUnleashed, #370).
 * Added `item_transform_random_horizontal_flip()` and `item_transform_random_vertical_flip()` for random geometric augmentation of dataset items with probability `p`, with support for detection and segmentation item types and datasets (@DerrickUnleashed, #381).
+* Added `item_transform_resize()` for resizing dataset items, with support for detection and segmentation item types and datasets (@srishtiii28, #362).
 * `item_transform_rotate()` now supports segmentation items and datasets, rotating the masks alongside the image (@srishtiii28, #379).
 * Added `item_transform_crop()` for cropping dataset items at a specified location and size, with support for detection and segmentation item types and datasets (@DerrickUnleashed, #371).
 * Added `item_transform_pad()` for padding dataset items on all sides, with support for detection, segmentation and rotated-box item types and datasets (@DerrickUnleashed, #373).
diff --git a/R/item-transforms-geometry.R b/R/item-transforms-geometry.R
index 1a2f7dbe..b3c53481 100644
--- a/R/item-transforms-geometry.R
+++ b/R/item-transforms-geometry.R
@@ -1146,3 +1146,106 @@ item_transform_perspective.image_with_segmentation_mask <- function(x, startpoin
   x
 }
 
+# item_transform_resize
+
+#' Resize a dataset item
+#'
+#' Resizes the image inside a dataset item. For detection items, bounding box
+#' coordinates are rescaled by the same factors as the image. For segmentation
+#' items, the masks are resized alongside the image with nearest-neighbour
+#' sampling, so that they keep their discrete values.
+#'
+#' @param x A dataset item, typically an \code{image_with_bounding_box} or
+#'   \code{image_with_segmentation_mask} object containing an image tensor
+#'   and associated target data.
+#' @inheritParams transform_resize
+#'
+#' @return A dataset item of the same class with the image and target resized.
+#'
+#' @examples
+#' \dontrun{
+#' url <- "https://upload.wikimedia.org/wikipedia/commons/b/b6/Felis_catus-cat_on_snow.jpg"
+#' img <- base_loader(url) |> transform_to_tensor()
+#'
+#' boxes <- torch_tensor(matrix(c(600, 200, 2880, 1860), ncol = 4), dtype = torch_float32())
+#'
+#' before <- list(x = img, y = list(boxes = boxes, labels = "cat"))
+#' class(before) <- c("image_with_bounding_box", "list")
+#'
+#' after <- item_transform_resize(before, size = c(600, 800))
+#'
+#' before_plot <- draw_bounding_boxes(before, colors = "blue", width = 10)
+#' after_plot <- draw_bounding_boxes(after, colors = "red", width = 10)
+#' tensor_image_browse(before_plot)
+#' tensor_image_browse(after_plot)
+#' }
+#'
+#' @family item_unitary_transforms
+#'
+#' @export
+item_transform_resize <- function(x, size, interpolation = 2) {
+  UseMethod("item_transform_resize", x)
+}
+
+#' @export
+item_transform_resize.default <- function(x, size, interpolation = 2) {
+  cli_abort(
+    "{.fn item_transform_resize} requires a dataset item (a list with {.var x} and {.var y} fields), not {.obj_type_friendly {x}}.
+    To resize a raw image tensor, use {.fn transform_resize} instead."
+  )
+}
+
+#' @export
+item_transform_resize.dataset <- function(x, size, interpolation = 2) {
+  original_getitem <- x$.getitem
+  unlockBinding(".getitem", as.environment(x))
+  x$.getitem <- function(index) {
+    item <- original_getitem(index)
+    item_transform_resize(item, size = size, interpolation = interpolation)
+  }
+  x
+}
+
+#' @export
+item_transform_resize.image_with_bounding_box <- function(x, size, interpolation = 2) {
+  orig_spatial <- tail(x$x$shape, 2)
+
+  x$x <- transform_resize(x$x, size, interpolation)
+
+  new_spatial <- tail(x$x$shape, 2)
+  scale_h <- new_spatial[1] / orig_spatial[1]
+  scale_w <- new_spatial[2] / orig_spatial[2]
+
+  boxes <- x$y$boxes$clone()
+  if (boxes$size(1) > 0) {
+    boxes[, 1] <- boxes[, 1] * scale_w
+    boxes[, 2] <- boxes[, 2] * scale_h
+    boxes[, 3] <- boxes[, 3] * scale_w
+    boxes[, 4] <- boxes[, 4] * scale_h
+  }
+  x$y$boxes <- boxes
+  x$y$image_height <- new_spatial[1]
+  x$y$image_width <- new_spatial[2]
+
+  x
+}
+
+#' @export
+item_transform_resize.image_with_segmentation_mask <- function(x, size, interpolation = 2) {
+  x$x <- transform_resize(x$x, size, interpolation)
+
+  new_spatial <- tail(x$x$shape, 2)
+  masks <- x$y$masks
+  x$y$masks <- if (masks$size(1) > 0) {
+    transform_resize(masks, new_spatial, interpolation = 0)
+  } else {
+    torch_zeros(c(0, new_spatial), dtype = masks$dtype, device = masks$device)
+  }
+  x$y$image_height <- new_spatial[1]
+  x$y$image_width <- new_spatial[2]
+
+  x
+}
+
+#' @export
+item_transform_resize.image_with_rotated_box <- item_transform_resize.image_with_bounding_box
diff --git a/man/item_transform_affine.Rd b/man/item_transform_affine.Rd
index 9af56432..32b0f18b 100644
--- a/man/item_transform_affine.Rd
+++ b/man/item_transform_affine.Rd
@@ -87,6 +87,7 @@ Other item_unitary_transforms:
 \code{\link[=item_transform_hflip]{item_transform_hflip()}},
 \code{\link[=item_transform_pad]{item_transform_pad()}},
 \code{\link[=item_transform_perspective]{item_transform_perspective()}},
+\code{\link[=item_transform_resize]{item_transform_resize()}},
 \code{\link[=item_transform_rotate]{item_transform_rotate()}},
 \code{\link[=item_transform_vflip]{item_transform_vflip()}}
 }
diff --git a/man/item_transform_center_crop.Rd b/man/item_transform_center_crop.Rd
index 71fb1d03..80e6bb08 100644
--- a/man/item_transform_center_crop.Rd
+++ b/man/item_transform_center_crop.Rd
@@ -57,6 +57,7 @@ Other item_unitary_transforms:
 \code{\link[=item_transform_hflip]{item_transform_hflip()}},
 \code{\link[=item_transform_pad]{item_transform_pad()}},
 \code{\link[=item_transform_perspective]{item_transform_perspective()}},
+\code{\link[=item_transform_resize]{item_transform_resize()}},
 \code{\link[=item_transform_rotate]{item_transform_rotate()}},
 \code{\link[=item_transform_vflip]{item_transform_vflip()}}
 }
diff --git a/man/item_transform_crop.Rd b/man/item_transform_crop.Rd
index b8e5a4a5..33fce70c 100644
--- a/man/item_transform_crop.Rd
+++ b/man/item_transform_crop.Rd
@@ -63,6 +63,7 @@ Other item_unitary_transforms:
 \code{\link[=item_transform_hflip]{item_transform_hflip()}},
 \code{\link[=item_transform_pad]{item_transform_pad()}},
 \code{\link[=item_transform_perspective]{item_transform_perspective()}},
+\code{\link[=item_transform_resize]{item_transform_resize()}},
 \code{\link[=item_transform_rotate]{item_transform_rotate()}},
 \code{\link[=item_transform_vflip]{item_transform_vflip()}}
 }
diff --git a/man/item_transform_hflip.Rd b/man/item_transform_hflip.Rd
index 65a3f0da..65b2b11f 100644
--- a/man/item_transform_hflip.Rd
+++ b/man/item_transform_hflip.Rd
@@ -47,6 +47,7 @@ Other item_unitary_transforms:
 \code{\link[=item_transform_crop]{item_transform_crop()}},
 \code{\link[=item_transform_pad]{item_transform_pad()}},
 \code{\link[=item_transform_perspective]{item_transform_perspective()}},
+\code{\link[=item_transform_resize]{item_transform_resize()}},
 \code{\link[=item_transform_rotate]{item_transform_rotate()}},
 \code{\link[=item_transform_vflip]{item_transform_vflip()}}
 }
diff --git a/man/item_transform_pad.Rd b/man/item_transform_pad.Rd
index f875e78d..074927fe 100644
--- a/man/item_transform_pad.Rd
+++ b/man/item_transform_pad.Rd
@@ -77,6 +77,7 @@ Other item_unitary_transforms:
 \code{\link[=item_transform_crop]{item_transform_crop()}},
 \code{\link[=item_transform_hflip]{item_transform_hflip()}},
 \code{\link[=item_transform_perspective]{item_transform_perspective()}},
+\code{\link[=item_transform_resize]{item_transform_resize()}},
 \code{\link[=item_transform_rotate]{item_transform_rotate()}},
 \code{\link[=item_transform_vflip]{item_transform_vflip()}}
 }
diff --git a/man/item_transform_perspective.Rd b/man/item_transform_perspective.Rd
index a94e0a31..d41dfc7d 100644
--- a/man/item_transform_perspective.Rd
+++ b/man/item_transform_perspective.Rd
@@ -77,6 +77,7 @@ Other item_unitary_transforms:
 \code{\link[=item_transform_crop]{item_transform_crop()}},
 \code{\link[=item_transform_hflip]{item_transform_hflip()}},
 \code{\link[=item_transform_pad]{item_transform_pad()}},
+\code{\link[=item_transform_resize]{item_transform_resize()}},
 \code{\link[=item_transform_rotate]{item_transform_rotate()}},
 \code{\link[=item_transform_vflip]{item_transform_vflip()}}
 }
diff --git a/man/item_transform_resize.Rd b/man/item_transform_resize.Rd
new file mode 100644
index 00000000..4bf43571
--- /dev/null
+++ b/man/item_transform_resize.Rd
@@ -0,0 +1,63 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/item-transforms-geometry.R
+\name{item_transform_resize}
+\alias{item_transform_resize}
+\title{Resize a dataset item}
+\usage{
+item_transform_resize(x, size, interpolation = 2)
+}
+\arguments{
+\item{x}{A dataset item, typically an \code{image_with_bounding_box} or
+\code{image_with_segmentation_mask} object containing an image tensor
+and associated target data.}
+
+\item{size}{(integer vector or integer): Desired output size. If \code{size} is
+an integer vector of length 2 like \code{c(h, w)}, output size will be matched
+to this. If \code{size} is a bare integer, smaller edge of the image will be
+matched to this number, i.e., if height > width, then image will be
+rescaled to \verb{(size * height / width, size)}.}
+
+\item{interpolation}{(integer, optional): Desired interpolation. An integer
+\code{0 = nearest}, \code{2 = bilinear}, and \code{3 = bicubic} or a name from
+\code{\link[magick:filter_types]{magick::filter_types()}}.}
+}
+\value{
+A dataset item of the same class with the image and target resized.
+}
+\description{
+Resizes the image inside a dataset item. For detection items, bounding box
+coordinates are rescaled by the same factors as the image. For segmentation
+items, the masks are resized alongside the image with nearest-neighbour
+sampling, so that they keep their discrete values.
+}
+\examples{
+\dontrun{
+url <- "https://upload.wikimedia.org/wikipedia/commons/b/b6/Felis_catus-cat_on_snow.jpg"
+img <- base_loader(url) |> transform_to_tensor()
+
+boxes <- torch_tensor(matrix(c(600, 200, 2880, 1860), ncol = 4), dtype = torch_float32())
+
+before <- list(x = img, y = list(boxes = boxes, labels = "cat"))
+class(before) <- c("image_with_bounding_box", "list")
+
+after <- item_transform_resize(before, size = c(600, 800))
+
+before_plot <- draw_bounding_boxes(before, colors = "blue", width = 10)
+after_plot <- draw_bounding_boxes(after, colors = "red", width = 10)
+tensor_image_browse(before_plot)
+tensor_image_browse(after_plot)
+}
+
+}
+\seealso{
+Other item_unitary_transforms:
+\code{\link[=item_transform_affine]{item_transform_affine()}},
+\code{\link[=item_transform_center_crop]{item_transform_center_crop()}},
+\code{\link[=item_transform_crop]{item_transform_crop()}},
+\code{\link[=item_transform_hflip]{item_transform_hflip()}},
+\code{\link[=item_transform_pad]{item_transform_pad()}},
+\code{\link[=item_transform_perspective]{item_transform_perspective()}},
+\code{\link[=item_transform_rotate]{item_transform_rotate()}},
+\code{\link[=item_transform_vflip]{item_transform_vflip()}}
+}
+\concept{item_unitary_transforms}
diff --git a/man/item_transform_rotate.Rd b/man/item_transform_rotate.Rd
index aad2d6d7..6afd79b1 100644
--- a/man/item_transform_rotate.Rd
+++ b/man/item_transform_rotate.Rd
@@ -72,6 +72,7 @@ Other item_unitary_transforms:
 \code{\link[=item_transform_hflip]{item_transform_hflip()}},
 \code{\link[=item_transform_pad]{item_transform_pad()}},
 \code{\link[=item_transform_perspective]{item_transform_perspective()}},
+\code{\link[=item_transform_resize]{item_transform_resize()}},
 \code{\link[=item_transform_vflip]{item_transform_vflip()}}
 }
 \concept{item_unitary_transforms}
diff --git a/man/item_transform_vflip.Rd b/man/item_transform_vflip.Rd
index 77943a4c..fafc26f7 100644
--- a/man/item_transform_vflip.Rd
+++ b/man/item_transform_vflip.Rd
@@ -48,6 +48,7 @@ Other item_unitary_transforms:
 \code{\link[=item_transform_hflip]{item_transform_hflip()}},
 \code{\link[=item_transform_pad]{item_transform_pad()}},
 \code{\link[=item_transform_perspective]{item_transform_perspective()}},
+\code{\link[=item_transform_resize]{item_transform_resize()}},
 \code{\link[=item_transform_rotate]{item_transform_rotate()}}
 }
 \concept{item_unitary_transforms}
diff --git a/tests/testthat/test-item-transforms-geometry.R b/tests/testthat/test-item-transforms-geometry.R
index 53f0f8c2..50d79007 100644
--- a/tests/testthat/test-item-transforms-geometry.R
+++ b/tests/testthat/test-item-transforms-geometry.R
@@ -1514,3 +1514,185 @@ test_that("item_transform_perspective applies to datasets", {
   expect_equal_to_r(item2$y$boxes[1, 4], 35)
 })
 
+# item_transform_resize
+
+test_that("item_transform_resize rejects non-item inputs", {
+  img <- torch_randn(3, 100, 200)
+  expect_error(
+    item_transform_resize(img, size = c(50, 100)),
+    "requires a dataset item"
+  )
+  expect_error(
+    item_transform_resize(42, size = c(50, 100)),
+    "requires a dataset item"
+  )
+})
+
+test_that("item_transform_resize resizes detection items and rescales boxes", {
+  boxes <- matrix(c(120, 70, 180, 130), ncol = 4)
+  item <- make_detection_item(boxes, image_size = c(200L, 400L))
+  original_img <- item$x$clone()
+  original_class <- class(item)
+
+  result <- item_transform_resize(item, size = c(100L, 200L))
+
+  expect_s3_class(result, "image_with_bounding_box")
+  expect_tensor_dtype(result$x, item$x$dtype)
+  expect_tensor_shape(result$x, c(3, 100, 200))
+  expect_equal(result$y$image_height, 100L)
+  expect_equal(result$y$image_width, 200L)
+  expect_true(torch_equal(result$x, transform_resize(original_img, size = c(100L, 200L))))
+  expect_equal_to_r(result$y$boxes[1, ], boxes[1, ] / 2)
+
+  # a bare integer size matches the smaller edge
+  bare <- item_transform_resize(item, size = 100)
+  expect_true(torch_equal(bare$x, result$x))
+  expect_true(torch_equal(bare$y$boxes, result$y$boxes))
+
+  # input is not mutated
+  expect_equal_to_r(item$x, as_array(original_img))
+  expect_equal_to_r(item$y$boxes, boxes)
+  expect_equal(class(item), original_class)
+})
+
+test_that("item_transform_resize rescales boxes with an anisotropic size", {
+  boxes <- matrix(c(
+    120, 70, 180, 130,
+    0, 0, 400, 200
+  ), ncol = 4, byrow = TRUE)
+  item <- make_detection_item(boxes, image_size = c(200L, 400L))
+
+  result <- item_transform_resize(item, size = c(100L, 100L))
+
+  expect_tensor_shape(result$x, c(3, 100, 100))
+  expect_tensor_shape(result$y$boxes, c(2, 4))
+  expect_equal_to_r(result$y$boxes[1, ], c(30, 35, 45, 65))
+  expect_equal_to_r(result$y$boxes[2, ], c(0, 0, 100, 100))
+})
+
+test_that("item_transform_resize preserves labels and handles empty boxes", {
+  labels <- torch_tensor(c(1L, 2L), dtype = torch_long())
+  item <- make_detection_item(
+    matrix(c(120, 70, 180, 130, 210, 80, 280, 120), ncol = 4, byrow = TRUE),
+    labels = labels,
+    image_size = c(200L, 400L)
+  )
+  result <- item_transform_resize(item, size = c(100L, 200L))
+
+  expect_equal_to_r(result$y$labels, as.integer(as_array(labels)))
+
+  item <- make_detection_item(
+    boxes = matrix(numeric(0), ncol = 4),
+    labels = torch_zeros(0L, dtype = torch_long())
+  )
+  result <- item_transform_resize(item, size = c(50L, 100L))
+
+  expect_tensor_shape(result$y$boxes, c(0, 4))
+  expect_tensor_dtype(result$y$boxes, torch_float())
+})
+
+test_that("item_transform_resize resizes segmentation items", {
+  item <- make_segmentation_item(image_size = c(200L, 400L), num_masks = 2L)
+  original_img <- item$x$clone()
+  original_masks <- item$y$masks$clone()
+  original_labels <- as.integer(as_array(item$y$labels))
+
+  result <- item_transform_resize(item, size = c(100L, 200L))
+
+  expect_s3_class(result, "image_with_segmentation_mask")
+  expect_tensor_dtype(result$x, item$x$dtype)
+  expect_tensor_shape(result$x, c(3, 100, 200))
+  expect_equal(result$y$image_height, 100L)
+  expect_equal(result$y$image_width, 200L)
+  expect_equal_to_r(result$y$labels, original_labels)
+  expect_true(torch_equal(result$x, transform_resize(original_img, size = c(100L, 200L))))
+
+  expected_masks <- transform_resize(original_masks, size = c(100L, 200L), interpolation = 0)
+  expect_tensor_shape(result$y$masks, c(2, 100, 200))
+  expect_tensor_dtype(result$y$masks, torch_bool())
+  expect_true(result$y$masks$equal(expected_masks))
+})
+
+test_that("item_transform_resize handles items without any mask", {
+  item <- make_segmentation_item(image_size = c(200L, 400L), num_masks = 0L)
+  result <- item_transform_resize(item, size = c(100L, 200L))
+
+  expect_tensor_shape(result$y$masks, c(0, 100, 200))
+  expect_tensor_dtype(result$y$masks, torch_bool())
+})
+
+test_that("item_transform_resize keeps masks aligned with the image for a bare integer size", {
+  item <- make_segmentation_item(image_size = c(101L, 199L), num_masks = 2L)
+  result <- item_transform_resize(item, size = 50)
+
+  expect_equal(result$y$masks$shape[2], result$x$shape[2])
+  expect_equal(result$y$masks$shape[3], result$x$shape[3])
+  expect_equal(result$y$image_height, result$x$shape[2])
+  expect_equal(result$y$image_width, result$x$shape[3])
+})
+
+test_that("item_transform_resize handles rotated boxes", {
+  boxes <- matrix(c(120, 70, 180, 130), ncol = 4)
+  item <- make_detection_item(boxes, image_size = c(200L, 400L))
+  rotated <- item_transform_rotate(item, angle = 30)
+  original_angle <- as_array(rotated$y$boxes[1, 5])
+
+  result <- item_transform_resize(rotated, size = c(100L, 200L))
+
+  expect_s3_class(result, "image_with_rotated_box")
+  expect_tensor_shape(result$y$boxes, c(1, 5))
+  expect_tensor_dtype(result$y$boxes, torch_float())
+  expect_equal_to_r(result$y$boxes[1, 5], original_angle)
+})
+
+test_that("item_transform_resize can be composed", {
+  boxes <- matrix(c(120, 70, 180, 130, 210, 80, 280, 120), ncol = 4, byrow = TRUE)
+  labels <- torch_tensor(c(1L, 2L), dtype = torch_long())
+  item <- make_detection_item(boxes, labels = labels, image_size = c(200L, 400L))
+
+  result <- item |>
+    item_transform_resize(size = c(100L, 200L)) |>
+    item_transform_hflip()
+
+  expect_s3_class(result, "image_with_bounding_box")
+  expect_tensor_shape(result$x, c(3, 100, 200))
+  expect_equal_to_r(result$y$labels, as.integer(as_array(labels)))
+  expect_equal_to_r(result$y$boxes[1, ], c(200 - 90, 35, 200 - 60, 65))
+  expect_equal_to_r(result$y$boxes[2, ], c(200 - 140, 40, 200 - 105, 60))
+})
+
+test_that("item_transform_resize works on detection and segmentation datasets", {
+  ds <- dataset(
+    name = "toy_detection",
+    initialize = function() {},
+    .getitem = function(index) {
+      make_detection_item(matrix(c(120, 70, 180, 130), ncol = 4), image_size = c(200L, 400L))
+    },
+    .length = function() 1L
+  )()
+
+  ds <- item_transform_resize(ds, size = c(100L, 200L))
+  item <- ds$.getitem(1)
+
+  expect_s3_class(item, "image_with_bounding_box")
+  expect_tensor_shape(item$x, c(3, 100, 200))
+  expect_equal_to_r(item$y$boxes[1, ], c(60, 35, 90, 65))
+  expect_equal(item$y$image_height, 100L)
+  expect_equal(item$y$image_width, 200L)
+
+  ds <- dataset(
+    name = "toy_segmentation",
+    initialize = function() {},
+    .getitem = function(index) {
+      make_segmentation_item(image_size = c(200L, 400L), num_masks = 2L)
+    },
+    .length = function() 1L
+  )()
+
+  ds <- item_transform_resize(ds, size = c(100L, 200L))
+  item <- ds$.getitem(1)
+
+  expect_s3_class(item, "image_with_segmentation_mask")
+  expect_tensor_shape(item$x, c(3, 100, 200))
+  expect_tensor_shape(item$y$masks, c(2, 100, 200))
+})