updated README.

Added Training details section.

README.md

@@ -38,13 +38,6 @@ This repository hosts the trained model for **FLAIR Abnormality Segmentation** i
 - **Task:** Image Segmentation
 - **License:** Apache 2.0
 
-## Performance
-
-The model achieved the following metrics on the test dataset:
-
-- **Dice Coefficient:** 0.843
-- **Intersection over Union (IoU):** 0.609
-
 ## Usage
 
 To use this model for inference, you can load it using the `tensorflow` library.
@@ -74,9 +67,22 @@ output = model(input_data)["output_0"]
 print("Output shape:", output.shape)
 ```
 
-## Dataset
+## Training Details
+
+### Compute
 
-The model was trained on the LGG Segmentation Dataset, which contains Brain MRI images with FLAIR abnormalities labeled for segmentation tasks. The dataset is available on [Kaggle](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation).
+- The model was trained on a GeForce 4070Ti GPU with 16GB VRAM.
+- Training completed in approximately 4.9 minutes over 24 epochs.
+
+### Dataset
+
+- The model was trained on the [LGG Segmentation Dataset](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation), which includes Brain MRI images labeled for FLAIR abnormality segmentation.
+- Only images with positive FLAIR abnormalities were selected for training.
+
+### Performance on test set
+
+- **Dice Coefficient:** 0.843
+- **Intersection over Union (IoU):** 0.609
 
 ## Citation