diff --git a/src/transformers/models/detr/modeling_detr.py b/src/transformers/models/detr/modeling_detr.py
index e5d2a04c1f467..c1a164b486ffd 100644
--- a/src/transformers/models/detr/modeling_detr.py
+++ b/src/transformers/models/detr/modeling_detr.py
@@ -1237,8 +1237,15 @@ def forward(
 
         >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
         >>> model = DetrModel.from_pretrained("facebook/detr-resnet-50")
+
+        >>> # prepare image for the model
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
+
+        >>> # forward pass
         >>> outputs = model(**inputs)
+
+        >>> # the last hidden states are the final query embeddings of the Transformer decoder
+        >>> # these are of shape (batch_size, num_queries, hidden_size)
         >>> last_hidden_states = outputs.last_hidden_state
         >>> list(last_hidden_states.shape)
         [1, 100, 256]
@@ -1389,6 +1396,7 @@ def forward(
 
         ```python
         >>> from transformers import DetrFeatureExtractor, DetrForObjectDetection
+        >>> import torch
         >>> from PIL import Image
         >>> import requests
 
@@ -1400,17 +1408,24 @@ def forward(
 
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)
-        >>> # model predicts bounding boxes and corresponding COCO classes
-        >>> bboxes, logits = outputs.pred_boxes, outputs.logits
 
-        >>> # get probability per object class and remove the no-object class
-        >>> probas_per_class = outputs.logits.softmax(-1)[:, :, :-1]
-        >>> objects_to_keep = probas_per_class.max(-1).values > 0.9
-
-        >>> ids, _ = probas_per_class.max(-1).indices[objects_to_keep].sort()
-        >>> labels = [model.config.id2label[id.item()] for id in ids]
-        >>> labels
-        ['cat', 'cat', 'couch', 'remote', 'remote']
+        >>> # convert outputs (bounding boxes and class logits) to COCO API
+        >>> target_sizes = torch.tensor([image.size[::-1]])
+        >>> results = feature_extractor.post_process(outputs, target_sizes=target_sizes)[0]
+
+        >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        ...     box = [round(i, 2) for i in box.tolist()]
+        ...     # let's only keep detections with score > 0.9
+        ...     if score > 0.9:
+        ...         print(
+        ...             f"Detected {model.config.id2label[label.item()]} with confidence "
+        ...             f"{round(score.item(), 3)} at location {box}."
+        ...         )
+        Detected remote with confidence 0.998 at location [40.16, 70.81, 175.55, 117.98]
+        Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66]
+        Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76]
+        Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93]
+        Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.72]
         ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -1552,9 +1567,14 @@ def forward(
         Examples:
 
         ```python
-        >>> from transformers import DetrFeatureExtractor, DetrForSegmentation
-        >>> from PIL import Image
+        >>> import io
         >>> import requests
+        >>> from PIL import Image
+        >>> import torch
+        >>> import numpy
+
+        >>> from transformers import DetrFeatureExtractor, DetrForSegmentation
+        >>> from transformers.models.detr.feature_extraction_detr import rgb_to_id
 
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
@@ -1562,20 +1582,23 @@ def forward(
         >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
         >>> model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
 
+        >>> # prepare image for the model
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
+
+        >>> # forward pass
         >>> outputs = model(**inputs)
-        >>> # model predicts COCO classes, bounding boxes, and masks
-        >>> logits = outputs.logits
-        >>> list(logits.shape)
-        [1, 100, 251]
-
-        >>> bboxes = outputs.pred_boxes
-        >>> list(bboxes.shape)
-        [1, 100, 4]
-
-        >>> masks = outputs.pred_masks
-        >>> list(masks.shape)
-        [1, 100, 200, 267]
+
+        >>> # use the `post_process_panoptic` method of `DetrFeatureExtractor` to convert to COCO format
+        >>> processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0)
+        >>> result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0]
+
+        >>> # the segmentation is stored in a special-format png
+        >>> panoptic_seg = Image.open(io.BytesIO(result["png_string"]))
+        >>> panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8)
+        >>> # retrieve the ids corresponding to each mask
+        >>> panoptic_seg_id = rgb_to_id(panoptic_seg)
+        >>> panoptic_seg_id.shape
+        (800, 1066)
         ```"""
 
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict