diff --git a/docs/how_to/detect_and_annotate.md b/docs/how_to/detect_and_annotate.md
index ca0c5666..b068a923 100644
--- a/docs/how_to/detect_and_annotate.md
+++ b/docs/how_to/detect_and_annotate.md
@@ -4,17 +4,29 @@ comments: true
 
 # Detect and Annotate
 
-Supervision offers a streamlined solution to effortlessly annotate predictions from a
-range of object detection and segmentation models. This guide demonstrates how to
-execute inference using the YOLOv8 model with either the
-[Inference](https://github.com/roboflow/inference) or
-[Ultralytics](https://github.com/ultralytics/ultralytics) packages. Following this,
-you'll learn how to import these predictions into Supervision for image annotation
-purposes.
+Supervision provides a seamless process for annotating predictions generated by various 
+object detection and segmentation models. This guide shows how to perform inference 
+with the  [Inference](https://github.com/roboflow/inference), 
+[Ultralytics](https://github.com/ultralytics/ultralytics) or 
+[Transformers](https://github.com/huggingface/transformers) packages. Following this,
+you'll learn how to import these predictions into Supervision and use them to annotate 
+source image.
 
 ## Run Inference
 
-First, you'll need to obtain predictions from your object detection or segmentation model.
+First, you'll need to obtain predictions from your object detection or segmentation 
+model.
+
+=== "Inference"
+
+    ```python
+    import cv2
+    from inference import get_model
+
+    model = get_model(model_id="yolov8n-640")
+    image = cv2.imread(<PATH TO IMAGE>)
+    results = model.infer(image)[0]
+    ```
 
 === "Ultralytics"
 
@@ -27,26 +39,52 @@ First, you'll need to obtain predictions from your object detection or segmentat
     results = model(image)[0]
     ```
 
-=== "Inference"
+=== "Transformers"
 
     ```python
-    import cv2
-    from inference.models.utils import get_roboflow_model
+    import torch
+    from PIL import Image
+    from transformers import DetrImageProcessor, DetrForObjectDetection
 
-    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
-    image = cv2.imread(<PATH TO IMAGE>)
-    results = model.infer(image)[0]
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+    image = Image.open(<PATH TO IMAGE>)
+    inputs = processor(images=image, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    width, height = image.size
+    target_size = torch.tensor([[height, width]])
+    results = processor.post_process_object_detection(
+        outputs=outputs, target_sizes=target_size)[0]
     ```
 
 ## Load Predictions into Supervision
 
 Now that we have predictions from a model, we can load them into Supervision.
 
+=== "Inference"
+
+    We can do so using the [`sv.Detections.from_inference`](detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
+
+    ```{ .py hl_lines="2 8" }
+    import cv2
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8n-640")
+    image = cv2.imread(<PATH TO IMAGE>)
+    results = model.infer(image)[0]
+    detections = sv.Detections.from_inference(results)
+    ```
+
 === "Ultralytics"
 
     We can do so using the [`sv.Detections.from_ultralytics`](detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
 
-    ```python
+    ```{ .py hl_lines="2 8" }
     import cv2
     import supervision as sv
     from ultralytics import YOLO
@@ -57,34 +95,123 @@ Now that we have predictions from a model, we can load them into Supervision.
     detections = sv.Detections.from_ultralytics(results)
     ```
 
-=== "Inference"
+=== "Transformers"
 
-    We can do so using the [`sv.Detections.from_inference`](detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
+    We can do so using the [`sv.Detections.from_transformers`](detection/core/#supervision.detection.core.Detections.from_transformers) method, which accepts model results from both detection and segmentation models.
 
-    ```python
-    import cv2
+    ```{ .py hl_lines="2 19" }
+    import torch
     import supervision as sv
-    from inference.models.utils import get_roboflow_model
+    from PIL import Image
+    from transformers import DetrImageProcessor, DetrForObjectDetection
 
-    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>
-    image = cv2.imread(<PATH TO IMAGE>)
-    results = model.infer(image)[0]
-    detections = sv.Detections.from_inference(results)
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+    image = Image.open(<PATH TO IMAGE>)
+    inputs = processor(images=image, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    width, height = image.size
+    target_size = torch.tensor([[height, width]])
+    results = processor.post_process_object_detection(
+        outputs=outputs, target_sizes=target_size)[0]
+    detections = sv.Detections.from_transformers(results)
     ```
 
-You can conveniently load predictions from other computer vision frameworks and libraries using:
+You can load predictions from other computer vision frameworks and libraries using:
 
 - [`from_deepsparse`](detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
 - [`from_detectron2`](detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
 - [`from_mmdetection`](detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
-- [`from_inference`](detection/core/#supervision.detection.core.Detections.from_inference) ([Roboflow Inference](https://github.com/roboflow/inference))
 - [`from_sam`](detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
-- [`from_transformers`](detection/core/#supervision.detection.core.Detections.from_transformers) ([HuggingFace Transformers](https://github.com/huggingface/transformers))
 - [`from_yolo_nas`](detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))
 
-## Annotate Image
+## Annotate Image with Detections
 
-Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](annotators/#supervision.annotators.core.LabelAnnotator) classes. If you are running the segmentation model [`sv.MaskAnnotator`](annotators/#supervision.annotators.core.MaskAnnotator) is a drop-in replacement for [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) that will allow you to draw masks instead of boxes.
+Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](annotators/#supervision.annotators.core.LabelAnnotator) classes.
+
+=== "Inference"
+
+    ```{ .py hl_lines="10-16" }
+    import cv2
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8n-640")
+    image = cv2.imread(<PATH TO IMAGE>)
+    results = model.infer(image)[0]
+    detections = sv.Detections.from_inference(results)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="10-16" }
+    import cv2
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    image = cv2.imread(<PATH TO IMAGE>)
+    results = model(image)[0]
+    detections = sv.Detections.from_ultralytics(results)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Transformers"
+
+    ```{ .py hl_lines="21-27" }
+    import torch
+    import supervision as sv
+    from PIL import Image
+    from transformers import DetrImageProcessor, DetrForObjectDetection
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+    image = Image.open(<PATH TO IMAGE>)
+    inputs = processor(images=image, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    width, height = image.size
+    target_size = torch.tensor([[height, width]])
+    results = processor.post_process_object_detection(
+        outputs=outputs, target_sizes=target_size)[0]
+    detections = sv.Detections.from_transformers(results)
+    
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+![basic-annotation](https://media.roboflow.com/supervision_annotate_example.png)
+
+## Display Custom Labels
+
+<TODO>
 
 === "Ultralytics"
 
@@ -102,9 +229,9 @@ Finally, we can annotate the image with the predictions. Since we are working wi
     label_annotator = sv.LabelAnnotator()
 
     labels = [
-        model.model.names[class_id]
-        for class_id
-        in detections.class_id
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(detections['class_name'], detections.confidence)
     ]
 
     annotated_image = bounding_box_annotator.annotate(
@@ -118,9 +245,9 @@ Finally, we can annotate the image with the predictions. Since we are working wi
     ```python
     import cv2
     import supervision as sv
-    from inference.models.utils import get_roboflow_model
+    from inference import get_model
 
-    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>
+    model = get_model(model_id="yolov8n-640")
     image = cv2.imread(<PATH TO IMAGE>)
     results = model.infer(image)[0]
     detections = sv.Detections.from_inference(results)
@@ -128,18 +255,20 @@ Finally, we can annotate the image with the predictions. Since we are working wi
     bounding_box_annotator = sv.BoundingBoxAnnotator()
     label_annotator = sv.LabelAnnotator()
 
+    labels = [
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(detections['class_name'], detections.confidence)
+    ]
+
     annotated_image = bounding_box_annotator.annotate(
         scene=image, detections=detections)
     annotated_image = label_annotator.annotate(
         scene=annotated_image, detections=detections)
     ```
 
-![Predictions plotted on an image](https://media.roboflow.com/supervision_annotate_example.png)
+![custom-label-annotation](https://media.roboflow.com/supervision-annotator-examples/label-annotator-example-purple.png)
 
-## Display Annotated Image
+## Annotate Image with Segmentations
 
-To display the annotated image in Jupyter Notebook or Google Colab, use the [`sv.plot_image`](utils/notebook/#supervision.utils.notebook.plot_image) function.
-
-```python
-sv.plot_image(annotated_image)
-```
+If you are running the segmentation model [`sv.MaskAnnotator`](annotators/#supervision.annotators.core.MaskAnnotator) is a drop-in replacement for [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) that will allow you to draw masks instead of boxes.
\ No newline at end of file