Update Example use in README.md

README.md

@@ -33,6 +33,30 @@ The following model checkpoints are provided by the Keras team. Full code exampl
 |------------------------------------|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | deeplab_v3_plus_resnet50_pascalvoc | 39.1M      | DeeplabV3Plus with a ResNet50 v2 backbone. Trained on PascalVOC 2012 Semantic segmentation task, which consists of 20 classes and one background class. This model achieves a final categorical accuracy of 89.34% and mIoU of 0.6391 on evaluation dataset. This preset is only comptabile with Keras 3. |
 
+
+## Example Use
+
+Load DeepLabv3+ presets a extension of DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries.
+
+```
+images = np.ones(shape=(1, 96, 96, 3))
+labels = np.zeros(shape=(1, 96, 96, 2))
+segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
+    "hf://keras/deeplab_v3_plus_resnet50_pascalvoc",
+)
+segmenter.predict(images)
+```
+Specify `num_classes` to load randomly initialized segmentation head.
+
+```
+segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
+    "hf://keras/deeplab_v3_plus_resnet50_pascalvoc",
+    num_classes=2,
+)
+segmenter.preprocessor.image_size = (96, 96)
+segmenter.fit(images, labels, epochs=3)
+segmenter.predict(images)  # Trained 2 class segmentation.
+```
 ## Model paper
 
 https://arxiv.org/abs/1802.02611