standardized YOLOS model card according to template in #36979

docs/source/en/model_doc/yolos.md

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+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+    <div class="flex flex-wrap space-x-1">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+    </div>
+    <div class="flex flex-wrap space-x-1">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
 
-# YOLOS
+## YOLOS
 
-<div class="flex flex-wrap space-x-1">
-<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
-<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
-<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
-</div>
+[YOLOS](https://huggingface.co/papers/2106.00666) proposes to just leverage the plain [Vision Transformer (ViT)](vit) for object detection, inspired by DETR. It turns out that a base-sized encoder-only Transformer can also achieve 42 AP on COCO, similar to DETR and much more complex frameworks such as Faster R-CNN.
+
+The model uses 100 learnable object queries and forces the ground-truth boxes to also be exactly 100, filling any extra slots with a special "no object" entry. During training, the Hungarian matching algorithm was used to create optimal one-to-one mappings between each of the queries and each of the annotations. Once each query is matched, the model is updated by applying a standard cross-entropy loss on the predicted classes and a box loss that mixes L1 distance with a generalized IoU term.
+
+You can find all the original YOLOS checkpoints under the [HUST Vision Lab](https://huggingface.co/hustvl/models?search=yolos) organization.
+
+> [!TIP]
+> This model wasa contributed by [nielsr](https://huggingface.co/nielsr).
+> Click on the YOLOS models in the right sidebar for more examples of how to apply YOLOS to different object detection tasks.
+
+The example below demonstrates how to detect objects with [`Pipeline`] or the [`AutoModel`] class.
 
-## Overview
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
-The YOLOS model was proposed in [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://huggingface.co/papers/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
-YOLOS proposes to just leverage the plain [Vision Transformer (ViT)](vit) for object detection, inspired by DETR. It turns out that a base-sized encoder-only Transformer can also achieve 42 AP on COCO, similar to DETR and much more complex frameworks such as Faster R-CNN.
+```py
+import torch
+from transformers import pipeline
 
-The abstract from the paper is the following:
+detector = pipeline(
+    task="object-detection",
+    model="hustvl/yolos-base",
+    torch_dtype=torch.float16,
+    device=0
+)
+detector("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
+```
+
+</hfoption>
+<hfoption id="Automodel">
 
-*Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-1k dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e.g., YOLOS-Base directly adopted from BERT-Base architecture can obtain 42.0 box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS.*
+```py
+import torch
+from PIL import Image
+import requests
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/yolos_architecture.png"
-alt="drawing" width="600"/>
+processor = AutoImageProcessor.from_pretrained("hustvl/yolos-base")
+model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-base").to("cuda")
 
-<small> YOLOS architecture. Taken from the <a href="https://huggingface.co/papers/2106.00666">original paper</a>.</small>
+url = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png"
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+inputs = processor(images=image, return_tensors="pt").to("cuda")
 
-This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/hustvl/YOLOS).
+with torch.no_grad():
+    outputs = model(**inputs)
+logits = outputs.logits.softmax(-1)
+scores, labels = logits[..., :-1].max(-1)
+boxes = outputs.pred_boxes
 
-## Using Scaled Dot Product Attention (SDPA)
+threshold = 0.3
+keep = scores[0] > threshold
 
-PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function 
-encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the 
-[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) 
-or the [GPU Inference](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention)
-page for more information.
+filtered_scores = scores[0][keep]
+filtered_labels = labels[0][keep]
+filtered_boxes  = boxes[0][keep]
 
-SDPA is used by default for `torch>=2.1.1` when an implementation is available, but you may also set 
-`attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
+width, height = image.size
+pixel_boxes = filtered_boxes * torch.tensor([width, height, width, height], device=boxes.device)
 
+for score, label, box in zip(filtered_scores, filtered_labels, pixel_boxes):
+    x0, y0, x1, y1 = box.tolist()
+    print(f"Label {model.config.id2label[label.item()]}: {score:.2f} at [{x0:.0f}, {y0:.0f}, {x1:.0f}, {y1:.0f}]")
 ```
-from transformers import AutoModelForObjectDetection
-model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-base", attn_implementation="sdpa", torch_dtype=torch.float16)
-...
+
+</hfoption>
+<hfoption id="transformers CLI">
+
+```bash
+echo "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png" \
+  | transformers run \
+      --task object-detection \
+      --model hustvl/yolos-base \
+      --device 0
 ```
 
-For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
+</hfoption>
+</hfoptions>
 
-On a local benchmark (A100-40GB, PyTorch 2.3.0, OS Ubuntu 22.04) with `float32` and `hustvl/yolos-base` model, we saw the following speedups during inference.
+Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
 
-|   Batch size |   Average inference time (ms), eager mode |   Average inference time (ms), sdpa model |   Speed up, Sdpa / Eager (x) |
-|--------------|-------------------------------------------|-------------------------------------------|------------------------------|
-|            1 |                                       106 |                                        76 |                      1.39 |
-|            2 |                                       154 |                                        90 |                      1.71 |
-|            4 |                                       222 |                                       116 |                      1.91 |
-|            8 |                                       368 |                                       168 |                      2.19 |
+The example below uses [`Pytorch dynamic quantization`](https://pytorch.org/docs/stable/quantization.html#dynamic-quantization) to only quantize the weights to int8.
 
-## Resources
+```py
+import torch
+from torch.quantization import quantize_dynamic
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
 
-A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with YOLOS.
+processor = AutoImageProcessor.from_pretrained("hustvl/yolos-base")
+model_fp32 = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-base")
 
-<PipelineTag pipeline="object-detection"/>
+model_int8 = quantize_dynamic(
+    model_fp32,
+    {torch.nn.Linear},
+    dtype=torch.qint8
+)
 
-- All example notebooks illustrating inference + fine-tuning [`YolosForObjectDetection`] on a custom dataset can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/YOLOS).
-- Scripts for finetuning [`YolosForObjectDetection`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
-- See also: [Object detection task guide](../tasks/object_detection)
+image = processor(
+    images=your_pil_image, 
+    return_tensors="pt"
+)
+with torch.no_grad():
+    outputs = model_int8(**image)
+```
+
+Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.
 
-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.
 
-<Tip>
+```py
+from transformers.utils.attention_visualizer import AttentionMaskVisualizer
+from PIL import Image
+import requests
 
-Use [`YolosImageProcessor`] for preparing images (and optional targets) for the model. Contrary to [DETR](detr), YOLOS doesn't require a `pixel_mask` to be created.
+visualizer = AttentionMaskVisualizer("hustvl/yolos-base")
+
+url   = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png"
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+
+visualizer(image)
+```
 
-</Tip>
+## Notes
+- For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
+- Use [`YolosImageProcessor`] for preparing images (and optional targets) for the model. Contrary to [DETR](detr), YOLOS doesn't require a `pixel_mask` to be created.
+
+## Resources
+
+- All example notebooks illustrating inference + fine-tuning [`YolosForObjectDetection`] on a custom dataset can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/YOLOS).
+- Scripts for finetuning [`YolosForObjectDetection`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
 
 ## YolosConfig