update readme

- added training section
- updated validation section
- adjusted heading levels

README.md

@@ -31,9 +31,14 @@ The repository re-upload can be found on my personal [GitHub](https://github.com
 
 The custom classification layers were initialized and trained from scratch using my dataset. This approach follows a transfer learning paradigm, where pretrained features are reused, and task-specific layers are optimized for new tasks.
 
-### Data Cleaning
+## Data Cleaning
+
 I've self-cleaned the dataset by removing ~17,000 images due to issues like redundant entries (e.g., traditional clothing that didn't fit the primary dataset focus) and mislabeled data (e.g., incorrect seasons or colors), using custom Python notebooks and manual edits.
 
+## Training
+
+Training was done using the 80/20 rule. This means 80% of the dataset was used for training the model, while the remaining 20% was reserved for validation to test the model’s performance on unseen data. This split helps prevent overfitting and ensures a good balance between having enough data for the model to learn from and enough data to evaluate the model’s accuracy.
+
 ## Metrics
 
 <table>
@@ -66,7 +71,7 @@ I've self-cleaned the dataset by removing ~17,000 images due to issues like redu
 
 ## Validation
 
-The validation set consists of images held out from training, including a mix of photos from my own clothing and publicly available images from the internet. This setup ensures an objective evaluation of the model's performance. Accuracy values represent top-1 accuracy for each task.
+The validation set consists of 20% of the dataset, held out from training. It includes a mix of photos from my own clothing and publicly available images from the internet. This setup ensures an objective evaluation of the model's performance. Accuracy values represent top-1 accuracy for each task.
 
 The relatively low accuracy for Base Colour and Season models can be attributed to an imbalanced distribution in the dataset:
 
@@ -81,7 +86,7 @@ These imbalances in the dataset highlight potential areas for improvement, such
 - This model is designed for image classification tasks related to fashion products, such as identifying article types, colors, seasonal attributes, and main usages.
 - This project should primarily be viewed as an educational resource, rather than being intended for real-world applications.
 
-### Limitations
+## Limitations
 
 - The model was trained on a small and imbalanced dataset.
 - It supports only one clothing item per image.