Update README.md

README.md

@@ -17,38 +17,38 @@ license: apache-2.0
 KB-VQA
 ├── Files: Various files required for the demo such as samples images, dissertation report ..etc.
 ├── models
-|   ├── deformable-detr-detic: DETIC Object Detection Model.
-|   ├── yolov5: YOLOv5 Object Detection Model.baseline)
+│   ├── deformable-detr-detic: DETIC Object Detection Model.
+│   ├── yolov5: YOLOv5 Object Detection Model.baseline)
 ├── my_model
-|   ├── KBVQA.py : This module is the central component for implementing the designed model architecture for the Knowledge-Based Visual Question Answering (KB-VQA) project.
-|   ├── state_manager.py: Manages the user interface and session state to facilitate the Run Inference tool of the Streamlit demo app.
+│   ├── KBVQA.py : This module is the central component for implementing the designed model architecture for the Knowledge-Based Visual Question Answering (KB-VQA) project.
+│   ├── state_manager.py: Manages the user interface and session state to facilitate the Run Inference tool of the Streamlit demo app.
 │   ├── LLAMA2
 │   │   ├── LLAMA2_model.py: Used for loading LLaMA-2 model to be fine-tuned.
 │   ├── captioner
 │   │   ├── image_captioning.py: Provides functionality for generating captions for images.
-|   ├── detector
+│   ├── detector
 │   │   ├── object_detection.py: Used to detect objects in images using object detection models.
-|   ├── fine_tuner
+│   ├── fine_tuner
 │   │   ├── fine_tuner.py: Main Fine-Tuning Script for LLaMa-2 Chat models.
 │   │   ├── fine_tuning_data_handler.py: Handles and prepares the data for fine-tuning LLaMA-2 Chat models.
 │   │   ├── fine_tuning_data
 │   │   │   ├──fine_tuning_data_detic.csv: Fine-tuning data prepared by the prompt engineering module using DETIC detector.
 │   │   │   ├──fine_tuning_data_yolov5.csv: Fine-tuning data prepared by the prompt engineering module using YOLOv5. detector.
-|   ├── results
+│   ├── results
 │   │   ├── Demo_Images: Contains a pool of images used for the demo app.
 │   │   ├── evaluation.py: Provides a comprehensive framework for evaluating the KB-VQA model.
 │   │   ├── demo.py: Provides a comprehensive framework for visualizing and demonstrating the results of the KB-VQA evaluation.
 │   │   ├── evaluation_results.xlsx : This file contains all the evaluation results based on the evaluation data.
-|   ├── tabs
+│   ├── tabs
 │   │   ├── home.py: Displays an introduction to the application with brief background along with the demo tools description.
 │   │   ├── results.py: Manages the interactive Streamlit demo for visualizing model evaluation results and analysis.
 │   │   ├── run_inference.py: Responsible for the 'run inference' tool to test and use the fine-tuned models.
 │   │   ├── model_arch.py: Displays the model architecture and accompanying abstract and design details
 │   │   ├── dataset_analysis.py: Provides tools for visualizing dataset analyses.
-|   ├── utilities
+│   ├── utilities
 │   │   ├── ui_manager.py: Manages the user interface for the Streamlit application, handling the creation and navigation of various tabs.
 │   │   ├── gen_utilities.py: Provides a collection of utility functions and classes commonly used across various parts
-|   ├── config (All Configurations files are kept separated and stored as ".py" for easy reading - this will change after the project submission.)
+│   ├── config (All Configurations files are kept separated and stored as ".py" for easy reading - this will change after the project submission.)
 │   │   ├── kbvqa_config.py: Configuration parameters for the main KB-VQA model.
 │   │   ├── LLAMA2_config.py: Configuration parameters for LLaMA-2 model.
 │   │   ├── captioning_config.py : Configuration parameters for the captioning model (InstructBLIP).