updated README.

Added Usage section.

README.md

@@ -44,3 +44,32 @@ 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.
+
+```bash
+# Clones the repository and install dependencies
+!git clone https://huggingface.co/preethamganesh/bms-flair-abnormality-segmentation-v1.0.0
+!pip install tensorflow
+
+# Imports TensorFlow
+import tensorflow as tf
+
+# Loads the pre-trained model from the cloned directory
+model_path = "bms-flair-abnormality-segmentation-v1.0.0"
+exported_model = tf.saved_model.load(model_path)
+
+# Retrieves the default serving function from the loaded model
+model = exported_model.signatures["serving_default"]
+
+# Prepares a dummy input tensor for inference (batch size: 1, height: 256, width: 256, channels: 3)
+input_data = tf.ones((1, 256, 256, 3), dtype=tf.float32)
+
+# Performs inference using the model. The output will be a dictionary, with the segmentation map in the key 'output_0'
+output = model(input_data)["output_0"]
+
+# Prints the shape of the output tensor for verification
+print("Output shape:", output.shape)
+```