Update app.py

app.py

@@ -84,10 +84,16 @@ def set_example_url(example: list) -> dict:
 title = """<h1 id="title">Face Mask Detection with YOLOS</h1>"""
 
 description = """
-The model used in this space is the fine-tuned version of the COCO trained [hustlv/yolos-small](https://huggingface.co/hustlv/yolos-small). This fine-tuned model was trained for 200 epochs on the [face-mask-dataset]() from Kaggle which consisted of 853 images.
 
-Links to HuggingFace Model:
-- [nickmuchi/yolos-small-finetuned-masks](https://huggingface.co/nickmuchi/yolos-small-finetuned-masks)  
+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).
+
+The 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 [face mask dataset]("https://www.kaggle.com/datasets/andrewmvd/face-mask-detection") from Kaggle. The dataset consists of 853 images of people with annotations categorised as "with mask","without mask" and "mask not worn correctly". The model was trained for 200 epochs on a single GPU.
+
+Links to HuggingFace Models:
+- [nickmuchi/yolos-small-finetuned-masks](https://huggingface.co/nickmuchi/yolos-small-finetuned-masks)
+- [hustlv/yolos-small](https://huggingface.co/hustlv/yolos-small)  
 """
 
 models = ["nickmuchi/yolos-small-finetuned-masks"]