diff --git a/docs/changelog.md b/docs/changelog.md
index 11bca7a7..adf05cb1 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -1,3 +1,11 @@
+### 0.4.0 <small>April 5, 2023</small> 
+
+- Added [[#46](https://github.com/roboflow/supervision/discussions/48)]: `Detections.empty` to allow easy creation of empty `Detections` objects.
+- Added [[#56](https://github.com/roboflow/supervision/pull/56)]: `Detections.from_roboflow` to allow easy creation of `Detections` objects from Roboflow API inference results.
+- Added [[#56](https://github.com/roboflow/supervision/pull/56)]: `plot_images_grid` to allow easy plotting of multiple images on single plot.
+- Added [[#56](https://github.com/roboflow/supervision/pull/56)]: initial support for Pascal VOC XML format with `detections_to_voc_xml` method.
+- Changed [[#56](https://github.com/roboflow/supervision/pull/56)]: `show_frame_in_notebook` refactored and renamed to `plot_image`.
+
 ### 0.3.2 <small>March 23, 2023</small> 
 
 - Changed [[#50](https://github.com/roboflow/supervision/issues/50)]: Allow `Detections.class_id` to be `None`. 
diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index 3e92378c..ed44b453 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Iterator, Optional, Tuple, Union
+from typing import Iterator, Optional, Tuple, Union, List
 
 import numpy as np
 
@@ -176,6 +176,31 @@ class Detections:
             .astype(int),
         )
 
+    @classmethod
+    def from_roboflow(cls, roboflow_result: dict, class_list: List[str]) -> Detections:
+        xyxy = []
+        confidence = []
+        class_id = []
+
+        for prediction in roboflow_result["predictions"]:
+            x = prediction["x"]
+            y = prediction["y"]
+            width = prediction["width"]
+            height = prediction["height"]
+            x_min = x - width / 2
+            y_min = y - height / 2
+            x_max = x_min + width
+            y_max = y_min + height
+            xyxy.append([x_min, y_min, x_max, y_max])
+            class_id.append(class_list.index(prediction["class"]))
+            confidence.append(prediction["confidence"])
+
+        return Detections(
+            xyxy=np.array(xyxy),
+            confidence=np.array(confidence),
+            class_id=np.array(class_id).astype(int)
+        )
+
     @classmethod
     def from_coco_annotations(cls, coco_annotation: dict) -> Detections:
         xyxy, class_id = [], []