Update warning messages reffering to post_process_object_detection (#24649)

* including the threshold alert in warning messages.

* Updating doc owlvit.md including post_process_object_detection function with threshold.

* fix

docs/source/en/model_doc/owlvit.md

@@ -50,17 +50,13 @@ OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses [CL
 >>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
 >>> target_sizes = torch.Tensor([image.size[::-1]])
 >>> # Convert outputs (bounding boxes and class logits) to COCO API
->>> results = processor.post_process(outputs=outputs, target_sizes=target_sizes)
-
+>>> results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes, threshold=0.1)
 >>> i = 0  # Retrieve predictions for the first image for the corresponding text queries
 >>> text = texts[i]
 >>> boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
-
->>> score_threshold = 0.1
 >>> for box, score, label in zip(boxes, scores, labels):
 ...     box = [round(i, 2) for i in box.tolist()]
-...     if score >= score_threshold:
-...         print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
+...     print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
 Detected a photo of a cat with confidence 0.707 at location [324.97, 20.44, 640.58, 373.29]
 Detected a photo of a cat with confidence 0.717 at location [1.46, 55.26, 315.55, 472.17]
 ```

src/transformers/models/conditional_detr/image_processing_conditional_detr.py

@@ -1250,7 +1250,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
         """
         logging.warning_once(
             "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
-            " `post_process_object_detection`",
+            " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
         )
 
         out_logits, out_bbox = outputs.logits, outputs.pred_boxes

src/transformers/models/deformable_detr/image_processing_deformable_detr.py

@@ -1248,7 +1248,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
         """
         logger.warning_once(
             "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
-            " `post_process_object_detection`.",
+            " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
         )
 
         out_logits, out_bbox = outputs.logits, outputs.pred_boxes

src/transformers/models/detr/image_processing_detr.py

@@ -1219,7 +1219,7 @@ class DetrImageProcessor(BaseImageProcessor):
         """
         logger.warning_once(
             "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
-            " `post_process_object_detection`",
+            " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
         )
 
         out_logits, out_bbox = outputs.logits, outputs.pred_boxes

src/transformers/models/owlvit/image_processing_owlvit.py

@@ -354,7 +354,7 @@ class OwlViTImageProcessor(BaseImageProcessor):
         # TODO: (amy) add support for other frameworks
         warnings.warn(
             "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
-            " `post_process_object_detection`",
+            " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
             FutureWarning,
         )
 

src/transformers/models/yolos/image_processing_yolos.py

@@ -1151,7 +1151,7 @@ class YolosImageProcessor(BaseImageProcessor):
         """
         logger.warning_once(
             "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
-            " `post_process_object_detection`",
+            " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
         )
 
         out_logits, out_bbox = outputs.logits, outputs.pred_boxes