Update README.md

README.md

@@ -35,17 +35,23 @@ It achieves the following results on the evaluation set:
 - Loss: 1.0194
 - Accuracy: 0.9699
 
-## Model description
-
-More information needed
+## Training and evaluation data
 
-## Intended uses & limitations
+This model was fine-tuned on [the Fruits-30 dataset](https://huggingface.co/datasets/VinayHajare/Fruits-30), a collection of images featuring 30 different types of fruits. Each image has been preprocessed and standardized to a size of 224x224 pixels for uniformity.
 
-More information needed
+### Dataset Composition
+- Number of Classes: 30
+- Image Resolution: 224x224 pixels
+- Total Images: 826
 
-## Training and evaluation data
+### Training and Evaluation Split
+The dataset was split into training and evaluation sets using dataset.train_test_split function with a 80/20 train-test split, resulting in:
+- Training Set: 660 images
+- Evaluation Set: 166 images
 
-More information needed
+### Splitting Strategy
+- The data was shuffled (shuffle=True) before splitting to ensure a random distribution of classes across the training and evaluation sets.
+- Additionally, stratification was applied based on the "label" column (stratify_by_column='label') to maintain a balanced class distribution across both sets. This helps prevent the model from biasing towards classes with more samples in the training data.
 
 ## Training procedure