add reference to SAM

paper/paper.bib

@@ -75,3 +75,14 @@ @article{liu_survey_2024
 	keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
 }
 
+@article{kirillov_segment_2023,
+	title = {Segment anything},
+	copyright = {arXiv.org perpetual, non-exclusive license},
+	url = {https://arxiv.org/abs/2304.02643},
+	doi = {10.48550/ARXIV.2304.02643},
+	abstract = {We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at https://segment-anything.com to foster research into foundation models for computer vision.},
+	urldate = {2024-08-01},
+	author = {Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Dollár, Piotr and Girshick, Ross},
+	year = {2023},
+	keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
+}

paper/paper.md

@@ -1,5 +1,5 @@
 ---
-title: 'Efficient Image Annotation with Annotate-Lab: An Open-Source Solution'
+title: 'Annotate-Lab: Simplifying Image Annotation'
 tags:
   - Image Annotation
   - Open-Source Tools
@@ -108,7 +108,7 @@ The downloaded configurations provides the regions information along with co-ord
    ]
 }
 ```
-This tool also supports the [YOLO format](https://docs.ultralytics.com/datasets/detect/#ultralytics-yolo-format). A dataset of ripe and unripe tomatoes has been created and can be found on [Kaggle](https://www.kaggle.com/datasets/sumn2u/riped-and-unriped-tomato-dataset). \autoref{fig:annotated_tomatoes} shows the original and annotated tomatoes from that dataset.
+This tool also supports the [YOLO format](https://docs.ultralytics.com/datasets/detect/#ultralytics-yolo-format). A dataset of ripe and unripe tomatoes has been created and can be found on [Kaggle](https://www.kaggle.com/datasets/sumn2u/riped-and-unriped-tomato-dataset). \autoref{fig:annotated_tomatoes} shows the original and annotated tomatoes from that dataset. Besides this, it also selects the bounding box using the Segment Anything Model (SAM) \autoref{kirillov_segment_2023}.
 
 ![Annotated Tomatoes \label{fig:annotated_tomatoes}](./annotated_tomatoes.png)