Update README.md

README.md

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 # Overview
-The best_model_for_identifying_frogs is a deep learning model designed to perform image recognition with a specific focus on identifying frogs within images. It is powered by the GPT-5 architecture, a state-of-the-art model developed by OpenAI. The model has been fine-tuned on a dataset containing various images of frogs to achieve high accuracy in detecting the presence of frogs in images.
+The *best_model_for_identifying_frogs* is a deep learning model designed to perform image recognition with a specific focus on identifying frogs within images. It is powered by the GPT-5 architecture, a state-of-the-art model developed by OpenAI. The model has been fine-tuned on a dataset containing various images of frogs to achieve high accuracy in detecting the presence of frogs in images.
 
 # Intended Use
-The primary purpose of the best_model_for_identifying_frogs is to assist users in automating the process of identifying frogs within images. It can be used in applications such as wildlife monitoring, ecological research, and biodiversity conservation efforts. The model is intended for use by researchers, conservationists, and developers who require reliable frog detection capabilities in their projects.
+The primary purpose of the *best_model_for_identifying_frogs* is to assist users in automating the process of identifying frogs within images. It can be used in applications such as wildlife monitoring, ecological research, and biodiversity conservation efforts. The model is intended for use by researchers, conservationists, and developers who require reliable frog detection capabilities in their projects.
 
 # Limitations and Ethical Considerations
-While the best_model_for_identifying_frogs demonstrates strong performance in detecting frogs in images, it may encounter limitations in certain scenarios. Some potential limitations include:
+While the *best_model_for_identifying_frogs* demonstrates strong performance in detecting frogs in images, it may encounter limitations in certain scenarios. Some potential limitations include:
 
 ## Limited Generalization: The model may not generalize well to images containing unusual perspectives, occlusions, or poor lighting conditions.
 Data Bias: The performance of the model may be influenced by the quality and diversity of the training data. It is important to consider potential biases in the dataset used for training.
-False Positives/Negatives: Like any machine learning model, the best_model_for_identifying_frogs may produce false positives (incorrectly identifying non-frogs as frogs) or false negatives (failing to detect frogs in images).
+False Positives/Negatives: Like any machine learning model, the *best_model_for_identifying_frogs* may produce false positives (incorrectly identifying non-frogs as frogs) or false negatives (failing to detect frogs in images).
 Users should exercise caution and perform manual verification when using the model in critical applications where the accuracy of frog detection is crucial. Additionally, it's important to adhere to ethical guidelines and ensure that the model is not used in ways that could harm wildlife or violate privacy rights.
 
 # Evaluation Metrics
-The performance of the best_model_for_identifying_frogs can be evaluated using standard image recognition metrics such as precision, recall, and F1-score. These metrics assess the model's ability to accurately detect frogs in images while minimizing false positives and false negatives. Additionally, qualitative assessments by domain experts can provide valuable insights into the model's performance in real-world scenarios.
+The performance of the *best_model_for_identifying_frogs* can be evaluated using standard image recognition metrics such as precision, recall, and F1-score. These metrics assess the model's ability to accurately detect frogs in images while minimizing false positives and false negatives. Additionally, qualitative assessments by domain experts can provide valuable insights into the model's performance in real-world scenarios.
 
 # Model Details
 Model Architecture: GPT-5
@@ -36,14 +36,14 @@ Output: Probability score indicating the likelihood of frogs present in the imag
 Training Data: A diverse dataset of images containing various species of frogs, annotated with labels indicating the presence or absence of frogs.
 Fine-Tuning Procedure: The GPT-5 model was fine-tuned using transfer learning on the frog image dataset, optimizing for high accuracy in frog detection.
 ## How to Use
-Users can utilize the best_model_for_identifying_frogs by following these steps:
+Users can utilize the *best_model_for_identifying_frogs* by following these steps:
 
 Input Image: Provide an image containing potential frog subjects as input to the model.
 Inference: Use the model to perform inference on the input image.
 Output: Receive a probability score indicating the likelihood of frogs present in the image.
 
 # Authors
-The best_model_for_identifying_frogs was developed by The Jedi Frogs.
+The *best_model_for_identifying_frogs* was developed by The Jedi Frogs.
 
 # License
 Very closed source and no right to reproduction