Update README.md

README.md

@@ -65,10 +65,10 @@ The baseline model is a **YOLOv12** multitask variant, extended with a **regress
 ## 🧪 Performance Tables
 
 ### Table 1: Performance of YOLOv12M at different resolutions
-> Include from paper (Table 3)
+> ![YOLOv12M at different resolutions](figures/YOLOv12M_table.jpg) 
 
 ### Table 2: YOLOv8 vs YOLOv12 on FPB
-> Include from paper (Table 4)
+> ![YOLOv8 vs YOLOv12 results](figures/training_results.png) 
 
 
 
@@ -79,7 +79,7 @@ Train the multi-task YOLOv12 model using `train.py`
 
 ## 🔍 Inference
 
-Download the trained best model from the [drive link](https://huggingface.co/datasets/issai/Food_Portion_Benchmark) and run inference on test images using `test.py`
+Download the trained best models from the [drive link](https://drive.google.com/drive/folders/1XbgdXzfX73PxUUxthcbcqbY-1TNRK51d?usp=sharing) and run inference on test images using `test.py`
 - Provide path to your images folder or image file
 - Replace `model` with the path to the downloaded model
 - Set `show=True` to save annotated images with bounding boxes and predicted weights
@@ -89,12 +89,14 @@ Download the trained best model from the [drive link](https://huggingface.co/dat
 
 ## 📚 In case of using our work in your research, please cite this paper
 
-<pre> @article{sanatbyeka2025multitask,
-  title     = {A Multitask Deep Learning Model for Food Scene Recognition and Portion Estimation},
-  author    = {Sanatbyeka, Aibota and Rakhimzhanova, Tomiris and Varol, Huseyin Atakan and Chan, Mei Yen},
-  journal   = {AI Open},
-  year      = {2025},
-  note      = {Preprint submitted April 7, 2025}
+<pre> @article{Sanatbyek_2025,
+    title={A multitask deep learning model for food scene recognition and portion estimation—the Food Portion Benchmark (FPB) dataset}, 
+    volume={13}, 
+    DOI={10.1109/access.2025.3603287}, 
+    journal={IEEE Access}, 
+    author={Sanatbyek, Aibota and Rakhimzhanova, Tomiris and Nurmanova, Bibinur and Omarova, Zhuldyz and Rakhmankulova, Aidana and Orazbayev, Rustem and Varol, Huseyin Atakan and Chan, Mei Yen}, 
+    year={2025}, 
+    pages={152033–152045}
 }
 </pre>