Update README.md

README.md

@@ -8,11 +8,11 @@ The image classification model employs a convolutional neural network (CNN) arch
 
 ## 3. How to Guide
 To use the image classification model:
-1. Prepare your dataset: Organize your images into folders based on their categories. Each folder should represent a different class.
-2. Data Preprocessing: Resize your images to a uniform size and perform normalization if necessary.
+1. Prepare dataset: Organize images into folders based on their categories. Each folder should represent a different class.
+2. Data Preprocessing: Resize images to a uniform size and perform normalization if necessary.
 3. Model Training: Train the image classification model using the prepared dataset. Adjust hyperparameters such as learning rate, batch size, and number of epochs as needed.
 4. Model Evaluation: Evaluate the trained model on a separate test dataset to assess its performance. Calculate metrics such as accuracy, precision, recall, and F1-score.
-5. Model Deployment: Deploy the trained model for inference on new unseen images. Integrate the model into your application or use it for batch processing.
+5. Model Deployment: Deploy the trained model for inference on new unseen images. Integrate the model into application or use it for batch processing.
 
 ## 4. License
 This project is licensed under the [MIT License](https://opensource.org/licenses/MIT). You are free to use, modify, and distribute the code for both commercial and non-commercial purposes. See the `LICENSE` file for more details.