Repository: huggingface/transformers
Pull request: [LWDETR] update model repos from stevenbucaille to AnnaZhang

# What does this PR do?

LWDetr got merged but I forgot to replace the model repos in the docs to a more "official" one.
As discussed with original authors, the checkpoints are available under [AnnaZhang](https://huggingface.co/AnnaZhang) profile. I just sent them an email about the merge, they should pull the latest version of the weights soon.

## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).

## Who can review?

@Cyrilvallez @stevhliu 

Changed files:
filename: docs/source/en/model_doc/lw_detr.md
patch: @@ -32,7 +32,7 @@ It enhances the DETR architecture for efficiency and speed using the following c
 3. Faster Decoder: Employs a shallow 3-layer DETR decoder with deformable cross-attention for lower latency and faster convergence.
 4. Optimized Queries: Uses a mixed-query scheme combining learnable content queries and generated spatial queries.
 
-You can find all the available Deformable DETR checkpoints under the [stevenbucaille](https://huggingface.co/stevenbucaille) organization.
+You can find all the available LW DETR checkpoints under the [AnnaZhang](https://huggingface.co/AnnaZhang) organization.
 The original code can be found [here](https://github.com/Atten4Vis/LW-DETR).
 
 > [!TIP]
@@ -52,7 +52,7 @@ import torch
 
 pipeline = pipeline(
     "object-detection", 
-    model="stevenbucaille/lwdetr_small_60e_coco",
+    model="AnnaZhang/lwdetr_small_60e_coco",
     dtype=torch.float16,
     device_map=0
 )
@@ -72,8 +72,8 @@ import torch
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 
-image_processor = AutoImageProcessor.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
-model = AutoModelForObjectDetection.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
+image_processor = AutoImageProcessor.from_pretrained("AnnaZhang/lwdetr_small_60e_coco")
+model = AutoModelForObjectDetection.from_pretrained("AnnaZhang/lwdetr_small_60e_coco")
 
 # prepare image for the model
 inputs = image_processor(images=image, return_tensors="pt")

filename: src/transformers/models/lw_detr/configuration_lw_detr.py
patch: @@ -33,7 +33,7 @@ class LwDetrViTConfig(BackboneConfigMixin, PreTrainedConfig):
     This is the configuration class to store the configuration of a [`LwDetrViTModel`]. It is used to instantiate an
     LW-DETR ViT model according to the specified arguments, defining the model architecture. Instantiating a configuration
     with the defaults will yield a similar configuration to that of the LW-DETR ViT
-    [stevenbucaille/lwdetr_small_60e_coco](https://huggingface.co/stevenbucaille/lwdetr_small_60e_coco) architecture.
+    [AnnaZhang/lwdetr_small_60e_coco](https://huggingface.co/AnnaZhang/lwdetr_small_60e_coco) architecture.
 
     LW-DETR ViT is the Vision Transformer backbone used in the LW-DETR model for real-time object detection. It features
     interleaved window and global attention mechanisms to reduce computational complexity while maintaining high performance.
@@ -171,7 +171,7 @@ class LwDetrConfig(PreTrainedConfig):
     This is the configuration class to store the configuration of a [`LwDetrModel`]. It is used to instantiate
     a LW-DETR model according to the specified arguments, defining the model architecture. Instantiating a
     configuration with the defaults will yield a similar configuration to that of the LW-DETR
-    [stevenbucaille/lwdetr_small_60e_coco](https://huggingface.co/stevenbucaille/lwdetr_small_60e_coco) architecture.
+    [AnnaZhang/lwdetr_small_60e_coco](https://huggingface.co/AnnaZhang/lwdetr_small_60e_coco) architecture.
 
     LW-DETR (Lightweight Detection Transformer) is a transformer-based object detection model designed for real-time
     detection tasks. It replaces traditional CNN-based detectors like YOLO with a more efficient transformer architecture
@@ -253,10 +253,10 @@ class LwDetrConfig(PreTrainedConfig):
     ```python
     >>> from transformers import LwDetrConfig, LwDetrModel
 
-    >>> # Initializing a LW-DETR stevenbucaille/lwdetr_small_60e_coco style configuration
+    >>> # Initializing a LW-DETR AnnaZhang/lwdetr_small_60e_coco style configuration
     >>> configuration = LwDetrConfig()
 
-    >>> # Initializing a model (with random weights) from the stevenbucaille/lwdetr_small_60e_coco style configuration
+    >>> # Initializing a model (with random weights) from the AnnaZhang/lwdetr_small_60e_coco style configuration
     >>> model = LwDetrModel(configuration)
 
     >>> # Accessing the model configuration

filename: src/transformers/models/lw_detr/convert_lw_detr_to_hf.py
patch: @@ -502,7 +502,7 @@ def main():
     )
     parser.add_argument("--checkpoint_path", type=str, help="Path to the checkpoint file (if not using hub download)")
     parser.add_argument("--push_to_hub", action="store_true", help="Push model to the hub")
-    parser.add_argument("--organization", type=str, default="stevenbucaille", help="Organization to push the model to")
+    parser.add_argument("--organization", type=str, default="AnnaZhang", help="Organization to push the model to")
 
     args = parser.parse_args()
 

filename: src/transformers/models/lw_detr/modeling_lw_detr.py
patch: @@ -1357,8 +1357,8 @@ def forward(
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> image_processor = AutoImageProcessor.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
-        >>> model = DeformableDetrModel.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
+        >>> image_processor = AutoImageProcessor.from_pretrained("AnnaZhang/lwdetr_small_60e_coco")
+        >>> model = DeformableDetrModel.from_pretrained("AnnaZhang/lwdetr_small_60e_coco")
 
         >>> inputs = image_processor(images=image, return_tensors="pt")
 
@@ -1609,8 +1609,8 @@ def forward(
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> image_processor = AutoImageProcessor.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
-        >>> model = LwDetrForObjectDetection.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
+        >>> image_processor = AutoImageProcessor.from_pretrained("AnnaZhang/lwdetr_small_60e_coco")
+        >>> model = LwDetrForObjectDetection.from_pretrained("AnnaZhang/lwdetr_small_60e_coco")
 
         >>> inputs = image_processor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)

filename: src/transformers/models/lw_detr/modular_lw_detr.py
patch: @@ -60,7 +60,7 @@ class LwDetrViTConfig(VitDetConfig):
     This is the configuration class to store the configuration of a [`LwDetrViTModel`]. It is used to instantiate an
     LW-DETR ViT model according to the specified arguments, defining the model architecture. Instantiating a configuration
     with the defaults will yield a similar configuration to that of the LW-DETR ViT
-    [stevenbucaille/lwdetr_small_60e_coco](https://huggingface.co/stevenbucaille/lwdetr_small_60e_coco) architecture.
+    [AnnaZhang/lwdetr_small_60e_coco](https://huggingface.co/AnnaZhang/lwdetr_small_60e_coco) architecture.
 
     LW-DETR ViT is the Vision Transformer backbone used in the LW-DETR model for real-time object detection. It features
     interleaved window and global attention mechanisms to reduce computational complexity while maintaining high performance.
@@ -200,7 +200,7 @@ class LwDetrConfig(PreTrainedConfig):
     This is the configuration class to store the configuration of a [`LwDetrModel`]. It is used to instantiate
     a LW-DETR model according to the specified arguments, defining the model architecture. Instantiating a
     configuration with the defaults will yield a similar configuration to that of the LW-DETR
-    [stevenbucaille/lwdetr_small_60e_coco](https://huggingface.co/stevenbucaille/lwdetr_small_60e_coco) architecture.
+    [AnnaZhang/lwdetr_small_60e_coco](https://huggingface.co/AnnaZhang/lwdetr_small_60e_coco) architecture.
 
     LW-DETR (Lightweight Detection Transformer) is a transformer-based object detection model designed for real-time
     detection tasks. It replaces traditional CNN-based detectors like YOLO with a more efficient transformer architecture
@@ -282,10 +282,10 @@ class LwDetrConfig(PreTrainedConfig):
     ```python
     >>> from transformers import LwDetrConfig, LwDetrModel
 
-    >>> # Initializing a LW-DETR stevenbucaille/lwdetr_small_60e_coco style configuration
+    >>> # Initializing a LW-DETR AnnaZhang/lwdetr_small_60e_coco style configuration
     >>> configuration = LwDetrConfig()
 
-    >>> # Initializing a model (with random weights) from the stevenbucaille/lwdetr_small_60e_coco style configuration
+    >>> # Initializing a model (with random weights) from the AnnaZhang/lwdetr_small_60e_coco style configuration
     >>> model = LwDetrModel(configuration)
 
     >>> # Accessing the model configuration
@@ -1288,8 +1288,8 @@ def forward(
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> image_processor = AutoImageProcessor.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
-        >>> model = DeformableDetrModel.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
+        >>> image_processor = AutoImageProcessor.from_pretrained("AnnaZhang/lwdetr_small_60e_coco")
+        >>> model = DeformableDetrModel.from_pretrained("AnnaZhang/lwdetr_small_60e_coco")
 
         >>> inputs = image_processor(images=image, return_tensors="pt")
 
@@ -1520,8 +1520,8 @@ def forward(
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> image_processor = AutoImageProcessor.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
-        >>> model = LwDetrForObjectDetection.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
+        >>> image_processor = AutoImageProcessor.from_pretrained("AnnaZhang/lwdetr_small_60e_coco")
+        >>> model = LwDetrForObjectDetection.from_pretrained("AnnaZhang/lwdetr_small_60e_coco")
 
         >>> inputs = image_processor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)