Update README.md

README.md

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   results: []
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 # YOLOS (small-sized) model
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 The original YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). 
 This model was further fine-tuned on the [license plate dataset]("https://www.kaggle.com/datasets/andrewmvd/car-plate-detection") from Kaggle. The dataset consists of 735 images of annotations categorised as "vehicle" and "license-plate". The model was trained for 200 epochs on a single GPU using Google Colab
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 ## Model description
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 YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN).
 ## Intended uses & limitations
 You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=hustvl/yolos) to look for all available YOLOS models.
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 ### How to use
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 Here is how to use this model:
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 ```python
 from transformers import YolosFeatureExtractor, YolosForObjectDetection
 from PIL import Image
@@ -44,14 +50,22 @@ logits = outputs.logits
 bboxes = outputs.pred_boxes
 ```
 Currently, both the feature extractor and model support PyTorch. 
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 ## Training data
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 The YOLOS model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. 
 ### Training
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 This model was fine-tuned for 200 epochs on the [license plate dataset]("https://www.kaggle.com/datasets/andrewmvd/car-plate-detection").
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 ## Evaluation results
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 This model achieves an AP (average precision) of **47.9**.
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 Accumulating evaluation results...
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 IoU metric: bbox
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 Metrics           | Metric Parameter      | Location    | Dets          | Value |
 ----------------  | --------------------- | ------------| ------------- | ----- |
 Average Precision | (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] | 0.479 |