Update README.md

README.md

@@ -1,31 +1,28 @@
-# Visual Question Answering using BLIP pre-trained model!
-
-This implementation applies the BLIP pre-trained model to solve the icon domain task. 
-![The BLIP model for VQA task](https://i.postimg.cc/ncnxSnJw/image.png)
-|  ![enter image description here](https://i.postimg.cc/1zSYsrmm/image.png)|  |
-|--|--|
-| How many dots are there? | 36 |
-
-# Description
-**Note: The test dataset does not have labels. I evaluated the model via Kaggle competition and got 96% in accuracy manner. Obviously, you can use a partition of the training set as a testing set.
-## Create data folder
-
-Copy all data following the example form
-You can download data [here](https://drive.google.com/file/d/1tt6qJbOgevyPpfkylXpKYy-KaT4_aCYZ/view?usp=sharing)
-
-## Install requirements.txt
-
-    pip install -r requirements.txt
-
-## Run finetuning code
-
-    python finetuning.py
-
-## Run prediction
-
-    python predicting.py
-
-### References:
-
-> Nguyen Van Tuan (2023). JAIST_Advanced Machine Learning_Visual_Question_Answering
-
+# Visual Question Answering using BLIP pre-trained model!
+
+This implementation applies the BLIP pre-trained model to solve the icon domain task. 
+![The BLIP model for VQA task](https://i.postimg.cc/ncnxSnJw/image.png)
+|  ![enter image description here](https://i.postimg.cc/1zSYsrmm/image.png)|  |
+|--|--|
+| How many dots are there? | 36 |
+
+# Description
+**Note: The test dataset does not have labels. I evaluated the model via Kaggle competition and got 96% in accuracy manner. Obviously, you can use a partition of the training set as a testing set.
+## Create data folder
+
+Copy all data following the example form
+You can download data [here](https://drive.google.com/file/d/1tt6qJbOgevyPpfkylXpKYy-KaT4_aCYZ/view?usp=sharing)
+
+## Install requirements.txt
+
+    pip install -r requirements.txt
+
+## Run finetuning code
+
+    python finetuning.py
+
+## Run prediction
+
+    python predicting.py
+
+