[Docs] Minor fixes (#21383)

* Improve docs

* Add DETA resources

---------
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

docs/source/en/_toctree.yml

@@ -293,8 +293,6 @@
         title: I-BERT
       - local: model_doc/jukebox
         title: Jukebox
-      - local: model_doc/layoutlm
-        title: LayoutLM
       - local: model_doc/led
         title: LED
       - local: model_doc/lilt
@@ -375,8 +373,6 @@
         title: T5
       - local: model_doc/t5v1.1
         title: T5v1.1
-      - local: model_doc/tapas
-        title: TAPAS
       - local: model_doc/tapex
         title: TAPEX
       - local: model_doc/transfo-xl
@@ -538,6 +534,8 @@
         title: GIT
       - local: model_doc/groupvit
         title: GroupViT
+      - local: model_doc/layoutlm
+        title: LayoutLM
       - local: model_doc/layoutlmv2
         title: LayoutLMV2
       - local: model_doc/layoutlmv3
@@ -554,6 +552,8 @@
         title: Perceiver
       - local: model_doc/speech-encoder-decoder
         title: Speech Encoder Decoder Models
+      - local: model_doc/tapas
+        title: TAPAS
       - local: model_doc/trocr
         title: TrOCR
       - local: model_doc/vilt

docs/source/en/model_doc/deta.mdx

@@ -26,9 +26,21 @@ Tips:
 
 - One can use [`DetaImageProcessor`] to prepare images and optional targets for the model.
 
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/deta_architecture.jpg"
+alt="drawing" width="600"/>
+
+<small> DETA overview. Taken from the <a href="https://arxiv.org/abs/2212.06137">original paper</a>. </small>
+
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/jozhang97/DETA).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DETA.
+
+- Demo notebooks for DETA can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETA).
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 
 ## DetaConfig
 

docs/source/en/model_doc/upernet.mdx

@@ -22,7 +22,7 @@ The abstract from the paper is the following:
 
 *Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes.*
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/transformers/model_doc/upernet_architecture.jpg"
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/upernet_architecture.jpg"
 alt="drawing" width="600"/>
 
 <small> UPerNet framework. Taken from the <a href="https://arxiv.org/abs/1807.10221">original paper</a>. </small>