Update README.md

README.md

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 # Generating Yu-Gi-Oh Monster Images and Text Using GAN, Stable Diffusion, and GIT Models
-We embarked on a machine learning experiment aimed at generating card images and text inspired by the titular card game created by Kazuki Takahashi, which embodies themes of grace, power, and peace.
+We initiated a machine learning experiment aimed at generating card images and text inspired by the iconic card game created by Kazuki Takahashi, characterized by themes of grace, power, and peace.
 
 ## Background / Objective 
-Our endeavor builds upon the diverse visual landscape presented by *Yu-Gi-Oh!* cards, which feature vibrant color schemes and distinct archetypes. Contrasting with the more uniform designs of Pokémon cards, this diversity offers an ideal dataset for testing and refining image generation techniques. 
+Our project builds upon the diverse visual landscape offered by Yu-Gi-Oh! cards, featuring vibrant color schemes and distinct archetypes. In contrast to the uniform designs of Pokémon cards, this diversity provides an excellent dataset for refining image generation techniques.
 
-Our project endeavors to push the boundaries of Generative Adversarial Network (GAN) technology, exploring its potential to generate intricate and varied visual elements. By doing so, we aim to contribute to advancements in both the AI sector and the gaming industry. While GANs have proven effective in producing detailed and diverse images, there remains room for improvement, especially in handling complex visual data such as Yu-Gi-Oh! cards.
+We aim to push the boundaries of Generative Adversarial Network (GAN) technology, exploring its potential to produce intricate and varied visual elements. Through this exploration, we seek to contribute to advancements in both the AI sector and the gaming industry. While GANs have shown effectiveness in generating detailed images, there remains room for improvement, particularly in handling complex visual data like Yu-Gi-Oh! cards.
 
-Through our unique research inquiries and experimental framework, our objective is to enhance text-to-image and image captioning models to generate images closely resembling those in our training dataset, with a particular emphasis on realism and naturalness.
+Our objective is to enhance text-to-image and image captioning models to generate images closely resembling those in our training dataset, with a focus on realism and naturalness.
 
 ## Research Questions
 **1. Refining Yu-Gi-Oh! Card Image Generation via GAN Model Optimization**
@@ -23,18 +23,21 @@ Can precise categorization of card attributes, particularly by archetype, in bot
 
 ## Experimental Design
 ### Data
-Initially, we sourced a total of 10,763 English Yu-Gi-Oh! cards using their official API, ensuring fair use. This dataset comprised original card images along with associated details such as card ID, name, description, and related information. Additionally, we acquired cropped images from within cards to train our GAN and stable diffusion models. Focusing on archetype testing, we finalized our dataset to include 4,252 training images and 1,064 testing images. While we refrain from publicly sharing this data due to potential ownership concerns, we have made these images accessible on the Hugging Face dataset to facilitate research in image generation challenges using GANs, particularly within the context of Yu-Gi-Oh! cards.
+We initially sourced a total of 10,763 English Yu-Gi-Oh! cards using their official API, ensuring fair use. This dataset included original card images along with associated details such as card ID, name, description, and related information. Additionally, cropped images from within cards were obtained to train our GAN and stable diffusion models. Focusing on archetype testing, we finalized our dataset to include 4,252 training images and 1,064 testing images. While refraining from publicly sharing this data due to potential ownership concerns, we have made these images accessible on the Hugging Face dataset to facilitate research in image generation challenges using GANs, particularly within the context of Yu-Gi-Oh! cards.
+
+#### Stable Diffusion Model Image Generation Example
+![Stable Diffusion Model Image Generation Example](https://github.com/nogibjj/Generating-Yu-Gi-Oh-Monsters-From-Archetypes/assets/141780408/46949cd7-d554-4915-8934-813e64c674e9)
+
+#### GIT Model Image Captioning Example
+![GIT Model Image Captioning Example](https://github.com/nogibjj/Generating-Yu-Gi-Oh-Monsters-From-Archetypes/assets/141780408/be2b1419-8215-42d5-b282-842dfd2aa91c)
 
 ### Streamlit App
-Leveraging insights garnered from exploratory data analysis (EDA) on both image and text data, as well as our understanding of GAN and stable diffusion models, we developed a Streamlit app to evaluate the performance of our fine-tuned models.
+Leveraging insights gained from exploratory data analysis (EDA) on both image and text data, as well as our understanding of GAN and stable diffusion models, we developed a Streamlit app to evaluate the performance of our fine-tuned models.
 
-#### Illustrative Examples of Stable Diffusion Model Image Generation
-![image](https://github.com/nogibjj/Generating-Yu-Gi-Oh-Monsters-From-Archetypes/assets/141780408/46949cd7-d554-4915-8934-813e64c674e9)
+![image](https://github.com/nogibjj/Generating-Yu-Gi-Oh-Monsters-From-Archetypes/assets/141780408/14ae9d07-9ab9-4e4c-91cb-f28325099fb7)
 
-#### Exemplary Results from GIT Model Image Captioning
-![image](https://github.com/nogibjj/Generating-Yu-Gi-Oh-Monsters-From-Archetypes/assets/141780408/be2b1419-8215-42d5-b282-842dfd2aa91c)
+## Setup
 
-## setup
 ```
 $ conda init 
 $ pip install -r requirements.txt