Synchronize

docs/source/en/main_classes/text_generation.mdx

@@ -24,7 +24,7 @@ of the generation method.
 
 To learn how to inspect a model's generation configuration, what are the defaults, how to change the parameters ad hoc,
 and how to create and save a customized generation configuration, refer to the
-[text generation strategies guide](./generation_strategies).
+[text generation strategies guide](../generation_strategies).
 
 ## GenerationConfig
 

docs/source/en/model_doc/mask2former.mdx

@@ -22,8 +22,8 @@ The abstract from the paper is the following:
 of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).*
 
 Tips:
-- Mask2Former uses the same preprocessing and postprocessing steps as [MaskFormer](maskformer). Use [`MaskFormerImageProcessor`] or [`AutoImageProcessor`] to prepare images and optional targets for the model.
-- To get the final segmentation, depending on the task, you can call [`~MaskFormerImageProcessor.post_process_semantic_segmentation`] or [`~MaskFormerImageProcessor.post_process_instance_segmentation`] or [`~MaskFormerImageProcessor.post_process_panoptic_segmentation`]. All three tasks can be solved using [`Mask2FormerForUniversalSegmentation`] output, panoptic segmentation accepts an optional `label_ids_to_fuse` argument to fuse instances of the target object/s (e.g. sky) together.
+- Mask2Former uses the same preprocessing and postprocessing steps as [MaskFormer](maskformer). Use [`Mask2FormerImageProcessor`] or [`AutoImageProcessor`] to prepare images and optional targets for the model.
+- To get the final segmentation, depending on the task, you can call [`~Mask2FormerImageProcessor.post_process_semantic_segmentation`] or [`~Mask2FormerImageProcessor.post_process_instance_segmentation`] or [`~Mask2FormerImageProcessor.post_process_panoptic_segmentation`]. All three tasks can be solved using [`Mask2FormerForUniversalSegmentation`] output, panoptic segmentation accepts an optional `label_ids_to_fuse` argument to fuse instances of the target object/s (e.g. sky) together.
 
 This model was contributed by [Shivalika Singh](https://huggingface.co/shivi) and [Alara Dirik](https://huggingface.co/adirik). The original code can be found [here](https://github.com/facebookresearch/Mask2Former).
 
@@ -55,3 +55,12 @@ The resource should ideally demonstrate something new instead of duplicating an
 
 [[autodoc]] Mask2FormerForUniversalSegmentation
     - forward
+
+## Mask2FormerImageProcessor
+
+[[autodoc]] Mask2FormerImageProcessor
+    - preprocess
+    - encode_inputs
+    - post_process_semantic_segmentation
+    - post_process_instance_segmentation
+    - post_process_panoptic_segmentation

docs/source/en/task_summary.mdx

@@ -85,11 +85,11 @@ Image classification labels an entire image from a predefined set of classes. Li
 ... )
 >>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
 >>> print(*preds, sep="\n")
-{'score': 0.4403, 'label': 'lynx, catamount'}
-{'score': 0.0343, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}
-{'score': 0.0321, 'label': 'snow leopard, ounce, Panthera uncia'}
-{'score': 0.0235, 'label': 'Egyptian cat'}
-{'score': 0.023, 'label': 'tiger cat'}
+{'score': 0.4335, 'label': 'lynx, catamount'}
+{'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}
+{'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}
+{'score': 0.0239, 'label': 'Egyptian cat'}
+{'score': 0.0229, 'label': 'tiger cat'}
 ```
 
 ### Object detection
@@ -131,10 +131,10 @@ Segmentation tasks are helpful in self-driving vehicles to create a pixel-level
 ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
 ... )
 >>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
->>> preds
-[{'score': 0.9856, 'label': 'LABEL_184'},
- {'score': 0.9976, 'label': 'snow'},
- {'score': 0.9962, 'label': 'cat'}]
+>>> print(*preds, sep="\n")
+{'score': 0.9879, 'label': 'LABEL_184'}
+{'score': 0.9973, 'label': 'snow'}
+{'score': 0.9972, 'label': 'cat'}
 ```
 
 ### Depth estimation