Update README.md with new model card content

README.md

@@ -1,17 +1,62 @@
 ---
 library_name: keras-hub
 ---
-This is a [`DepthAnything` model](https://keras.io/api/keras_hub/models/depth_anything) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-Model config:
-* **name:** depth_anything_backbone
-* **trainable:** True
-* **dtype:** {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}
-* **image_encoder:** {'module': 'keras_hub.src.models.dinov2.dinov2_backbone', 'class_name': 'DINOV2Backbone', 'config': {'name': 'dinov2_backbone', 'trainable': True, 'dtype': {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}, 'patch_size': 14, 'num_layers': 24, 'hidden_dim': 1024, 'num_heads': 16, 'intermediate_dim': 4096, 'layer_scale_init_value': 1.0, 'num_register_tokens': 0, 'use_mask_token': True, 'use_swiglu_ffn': False, 'dropout_rate': 0.0, 'drop_path_rate': 0.0, 'image_shape': [518, 518, 3], 'position_embedding_shape': [518, 518], 'antialias_in_interpolation': False, 'apply_layernorm': True}, 'registered_name': 'keras_hub>DINOV2Backbone'}
-* **reassemble_factors:** [4, 2, 1, 0.5]
-* **neck_hidden_dims:** [256, 512, 1024, 1024]
-* **fusion_hidden_dim:** 256
-* **head_hidden_dim:** 32
-* **head_in_index:** -1
-* **feature_keys:** ['stage5', 'stage12', 'stage18', 'stage24']
-
-This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information.
+### Model Overview
+
+
+## Example Usage
+```python
+import keras
+import numpy as np
+import requests
+from PIL import Image
+
+from keras_hub.src.models.depth_anything.depth_anything_depth_estimator import (
+    DepthAnythingDepthEstimator,
+)
+
+image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+image = image.resize((518, 518))
+depth_estimator = DepthAnythingDepthEstimator.from_preset(
+    "depth_anything_v2_base,
+    depth_estimation_type="relative",
+    max_depth=None,
+)
+images = np.expand_dims(np.array(image).astype("float32"), axis=0)
+outputs = depth_estimator.predict({"images": images})["depths"]
+depth = keras.ops.nn.relu(outputs[0, ..., 0])
+depth = (depth - keras.ops.min(depth)) / (
+    keras.ops.max(depth) - keras.ops.min(depth)
+)
+depth = keras.ops.convert_to_numpy(depth) * 255
+Image.fromarray(depth.astype("uint8")).save("depth_map.png")
+```
+
+## Example Usage with Hugging Face URI
+
+```python
+import keras
+import numpy as np
+import requests
+from PIL import Image
+
+from keras_hub.src.models.depth_anything.depth_anything_depth_estimator import (
+    DepthAnythingDepthEstimator,
+)
+
+image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+image = image.resize((518, 518))
+depth_estimator = DepthAnythingDepthEstimator.from_preset(
+    "depth_anything_v2_base,
+    depth_estimation_type="relative",
+    max_depth=None,
+)
+images = np.expand_dims(np.array(image).astype("float32"), axis=0)
+outputs = depth_estimator.predict({"images": images})["depths"]
+depth = keras.ops.nn.relu(outputs[0, ..., 0])
+depth = (depth - keras.ops.min(depth)) / (
+    keras.ops.max(depth) - keras.ops.min(depth)
+)
+depth = keras.ops.convert_to_numpy(depth) * 255
+Image.fromarray(depth.astype("uint8")).save("depth_map.png")
+```