diff --git a/CHANGELOG.md b/CHANGELOG.md
index e986a3a5fc..4256c8623b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -17,6 +17,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `VisionDataModule` as parent class for `BinaryMNISTDataModule`, `CIFAR10DataModule`, `FashionMNISTDataModule`, 
   and `MNISTDataModule` ([#400](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/400))
 
+- Added GIoU loss ([#347](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/347))
+
 ### Changed
 
 - Decoupled datamodules from models ([#332](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/332), [#270](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/270))
diff --git a/docs/source/losses.rst b/docs/source/losses.rst
index 44b401dfcc..901bb71964 100644
--- a/docs/source/losses.rst
+++ b/docs/source/losses.rst
@@ -13,6 +13,20 @@ We're cleaning up many of our losses, but in the meantime, submit a PR to add yo
 
 -------------
 
+Object Detection
+======================
+These are common losses used in object detection.
+
+---------------
+
+GIoU Loss
+---------
+
+.. autofunction:: pl_bolts.losses.object_detection.giou_loss
+    :noindex:
+
+---------------
+
 Reinforcement Learning
 ======================
 These are common losses used in RL.
diff --git a/pl_bolts/losses/object_detection.py b/pl_bolts/losses/object_detection.py
new file mode 100644
index 0000000000..81d0404813
--- /dev/null
+++ b/pl_bolts/losses/object_detection.py
@@ -0,0 +1,34 @@
+"""
+Generalized Intersection over Union (GIoU) loss (Rezatofighi et. al)
+"""
+
+import torch
+
+from pl_bolts.metrics.object_detection import giou
+
+
+def giou_loss(preds: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+    """
+    Calculates the generalized intersection over union loss.
+
+    It has been proposed in `Generalized Intersection over Union: A Metric and A
+    Loss for Bounding Box Regression <https://arxiv.org/abs/1902.09630>`_.
+
+    Args:
+        preds: batch of prediction bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
+        target: batch of target bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
+
+    Example:
+
+        >>> import torch
+        >>> from pl_bolts.losses.object_detection import giou_loss
+        >>> preds = torch.tensor([[100, 100, 200, 200]])
+        >>> target = torch.tensor([[150, 150, 250, 250]])
+        >>> giou_loss(preds, target)
+        tensor([[1.0794]])
+
+    Returns:
+        GIoU loss
+    """
+    loss = 1 - giou(preds, target)
+    return loss
diff --git a/pl_bolts/metrics/object_detection.py b/pl_bolts/metrics/object_detection.py
new file mode 100644
index 0000000000..3175f3ce24
--- /dev/null
+++ b/pl_bolts/metrics/object_detection.py
@@ -0,0 +1,42 @@
+import torch
+
+
+def giou(preds: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+    """
+    Calculates the generalized intersection over union.
+
+    It has been proposed in `Generalized Intersection over Union: A Metric and A
+    Loss for Bounding Box Regression <https://arxiv.org/abs/1902.09630>`_.
+
+    Args:
+        preds: batch of prediction bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
+        target: batch of target bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
+
+    Example:
+
+        >>> import torch
+        >>> from pl_bolts.metrics.object_detection import giou
+        >>> preds = torch.tensor([[100, 100, 200, 200]])
+        >>> target = torch.tensor([[150, 150, 250, 250]])
+        >>> giou(preds, target)
+        tensor([[-0.0794]])
+
+    Returns:
+        GIoU value
+    """
+    x_min = torch.max(preds[:, None, 0], target[:, 0])
+    y_min = torch.max(preds[:, None, 1], target[:, 1])
+    x_max = torch.min(preds[:, None, 2], target[:, 2])
+    y_max = torch.min(preds[:, None, 3], target[:, 3])
+    intersection = (x_max - x_min).clamp(min=0) * (y_max - y_min).clamp(min=0)
+    pred_area = (preds[:, 2] - preds[:, 0]) * (preds[:, 3] - preds[:, 1])
+    target_area = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
+    union = pred_area[:, None] + target_area - intersection
+    C_x_min = torch.min(preds[:, None, 0], target[:, 0])
+    C_y_min = torch.min(preds[:, None, 1], target[:, 1])
+    C_x_max = torch.max(preds[:, None, 2], target[:, 2])
+    C_y_max = torch.max(preds[:, None, 3], target[:, 3])
+    C_area = (C_x_max - C_x_min).clamp(min=0) * (C_y_max - C_y_min).clamp(min=0)
+    iou = torch.true_divide(intersection, union)
+    giou = iou - torch.true_divide((C_area - union), C_area)
+    return giou
diff --git a/tests/losses/test_object_detection.py b/tests/losses/test_object_detection.py
new file mode 100644
index 0000000000..30f0ab4576
--- /dev/null
+++ b/tests/losses/test_object_detection.py
@@ -0,0 +1,23 @@
+"""
+Test Object Detection Loss Functions
+"""
+
+import pytest
+import torch
+
+from pl_bolts.losses.object_detection import giou_loss
+
+
+@pytest.mark.parametrize("preds, target, expected_loss", [
+    (torch.tensor([[100, 100, 200, 200]]), torch.tensor([[100, 100, 200, 200]]), torch.tensor([0.0]))
+])
+def test_complete_overlap(preds, target, expected_loss):
+    torch.testing.assert_allclose(giou_loss(preds, target), expected_loss)
+
+
+@pytest.mark.parametrize("preds, target, expected_loss", [
+    (torch.tensor([[100, 100, 200, 200]]), torch.tensor([[100, 200, 200, 300]]), torch.tensor([1.0])),
+    (torch.tensor([[100, 100, 200, 200]]), torch.tensor([[200, 200, 300, 300]]), torch.tensor([1.5])),
+])
+def test_no_overlap(preds, target, expected_loss):
+    torch.testing.assert_allclose(giou_loss(preds, target), expected_loss)
diff --git a/tests/metrics/__init__.py b/tests/metrics/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tests/metrics/test_object_detection.py b/tests/metrics/test_object_detection.py
new file mode 100644
index 0000000000..a998502314
--- /dev/null
+++ b/tests/metrics/test_object_detection.py
@@ -0,0 +1,23 @@
+"""
+Test Object Detection Metric Functions
+"""
+
+import pytest
+import torch
+
+from pl_bolts.metrics.object_detection import giou
+
+
+@pytest.mark.parametrize("preds, target, expected_giou", [
+    (torch.tensor([[100, 100, 200, 200]]), torch.tensor([[100, 100, 200, 200]]), torch.tensor([1.0]))
+])
+def test_complete_overlap(preds, target, expected_giou):
+    torch.testing.assert_allclose(giou(preds, target), expected_giou)
+
+
+@pytest.mark.parametrize("preds, target, expected_giou", [
+    (torch.tensor([[100, 100, 200, 200]]), torch.tensor([[100, 200, 200, 300]]), torch.tensor([0.0])),
+    (torch.tensor([[100, 100, 200, 200]]), torch.tensor([[200, 200, 300, 300]]), torch.tensor([-0.5])),
+])
+def test_no_overlap(preds, target, expected_giou):
+    torch.testing.assert_allclose(giou(preds, target), expected_giou)