update docstring examples

keras_hub/src/models/deeplab_v3/deeplab_v3_segmenter.py

@@ -31,22 +31,24 @@ class DeepLabV3ImageSegmenter(ImageSegmenter):
     Load a DeepLabV3 preset with all the 21 class, pretrained segmentation head.
     ```python
     images = np.ones(shape=(1, 96, 96, 3))
-    labels = np.zeros(shape=(1, 96, 96, 1))
+    labels = np.zeros(shape=(1, 96, 96, 2))
     segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
-        "deeplabv3_resnet50_pascalvoc",
+        "deeplab_v3_plus_resnet50_pascalvoc",
     )
     segmenter.predict(images)
     ```
 
     Specify `num_classes` to load randomly initialized segmentation head.
     ```python
     segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
-        "deeplabv3_resnet50_pascalvoc",
+        "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.
     ```
+
     Load DeepLabv3+ presets a extension of DeepLabv3 by adding a simple yet
     effective decoder module to refine the segmentation results especially
     along object boundaries.

keras_hub/src/models/densenet/densenet_backbone.py

@@ -29,7 +29,7 @@ class DenseNetBackbone(FeaturePyramidBackbone):
     input_data = np.ones(shape=(8, 224, 224, 3))
 
     # Pretrained backbone
-    model = keras_hub.models.DenseNetBackbone.from_preset("densenet121_imagenet")
+    model = keras_hub.models.DenseNetBackbone.from_preset("densenet_121_imagenet")
     model(input_data)
 
     # Randomly initialized backbone with a custom config

keras_hub/src/models/sam/sam_backbone.py

@@ -68,7 +68,6 @@ class SAMBackbone(Backbone):
         image_encoder=image_encoder,
         prompt_encoder=prompt_encoder,
         mask_decoder=mask_decoder,
-        image_shape=(image_size, image_size, 3),
     )
     backbone(input_data)
     ```

keras_hub/src/models/sam/sam_image_segmenter.py

@@ -51,9 +51,9 @@ class SAMImageSegmenter(ImageSegmenter):
             (batch_size, 0, image_size, image_size, 1)
         ),
     }
-    # todo: update preset name
-    sam = keras_hub.models.SAMImageSegmenter.from_preset(`sam_base`)
-    sam(input_data)
+    sam = keras_hub.models.SAMImageSegmenter.from_preset('sam_base_sa1b')
+    outputs = sam.predict(input_data)
+    masks, iou_pred = outputs["masks"], outputs["iou_pred"]
     ```
 
     Load segment anything image segmenter with custom backbone
@@ -65,7 +65,7 @@ class SAMImageSegmenter(ImageSegmenter):
         (batch_size, image_size, image_size, 3),
         dtype="float32",
     )
-    image_encoder = ViTDetBackbone(
+    image_encoder = keras_hub.models.ViTDetBackbone(
         hidden_size=16,
         num_layers=16,
         intermediate_dim=16 * 4,
@@ -76,7 +76,7 @@ class SAMImageSegmenter(ImageSegmenter):
         window_size=2,
         image_shape=(image_size, image_size, 3),
     )
-    prompt_encoder = SAMPromptEncoder(
+    prompt_encoder = keras_hub.layers.SAMPromptEncoder(
         hidden_size=8,
         image_embedding_size=(8, 8),
         input_image_size=(
@@ -85,7 +85,7 @@ class SAMImageSegmenter(ImageSegmenter):
         ),
         mask_in_channels=16,
     )
-    mask_decoder = SAMMaskDecoder(
+    mask_decoder = keras_hub.layers.SAMMaskDecoder(
         num_layers=2,
         hidden_size=8,
         intermediate_dim=32,
@@ -95,13 +95,12 @@ class SAMImageSegmenter(ImageSegmenter):
         iou_head_depth=3,
         iou_head_hidden_dim=8,
     )
-    backbone = SAMBackbone(
+    backbone = keras_hub.models.SAMBackbone(
         image_encoder=image_encoder,
         prompt_encoder=prompt_encoder,
         mask_decoder=mask_decoder,
-        image_shape=(image_size, image_size, 3),
     )
-    sam = SAMImageSegmenter(
+    sam = keras_hub.models.SAMImageSegmenter(
         backbone=backbone
     )
     ```
@@ -115,7 +114,7 @@ class SAMImageSegmenter(ImageSegmenter):
     labels = np.array([[1., 0.]])
     box = np.array([[[[384., 384.], [640., 640.]]]])
     input_mask = np.ones((1, 1, 256, 256, 1))
-    Prepare an input dictionary:
+    # Prepare an input dictionary:
     inputs = {
         "images": image,
         "points": points,

keras_hub/src/models/vgg/vgg_backbone.py

@@ -27,7 +27,7 @@ class VGGBackbone(Backbone):
     input_data = np.ones((2, 224, 224, 3), dtype="float32")
 
     # Pretrained VGG backbone.
-    model = keras_hub.models.VGGBackbone.from_preset("vgg16")
+    model = keras_hub.models.VGGBackbone.from_preset("vgg_16_imagenet")
     model(input_data)
 
     # Randomly initialized VGG backbone with a custom config.