Commit
•
9a88928
1
Parent(s):
223099b
Update README.md with new model card content
Browse files
README.md
CHANGED
|
@@ -36,7 +36,7 @@ The following model checkpoints are provided by the Keras team. Weights have bee
|
|
| 36 |
input_data = np.ones(shape=(2, 224, 224, 3))
|
| 37 |
|
| 38 |
# Pretrained backbone
|
| 39 |
-
model = keras_hub.models.VGGBackbone.from_preset("
|
| 40 |
model(input_data)
|
| 41 |
|
| 42 |
# Randomly initialized backbone with a custom config
|
|
@@ -47,7 +47,7 @@ model = keras_hub.models.VGGBackbone(
|
|
| 47 |
model(input_data)
|
| 48 |
|
| 49 |
# Use VGG for image classification task
|
| 50 |
-
model = keras_hub.models.ImageClassifier.from_preset("
|
| 51 |
|
| 52 |
# User Timm presets directly from HuggingFace
|
| 53 |
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/vgg11.tv_in1k')
|
|
@@ -59,7 +59,7 @@ model = keras_hub.models.ImageClassifier.from_preset('hf://timm/vgg11.tv_in1k')
|
|
| 59 |
input_data = np.ones(shape=(2, 224, 224, 3))
|
| 60 |
|
| 61 |
# Pretrained backbone
|
| 62 |
-
model = keras_hub.models.VGGBackbone.from_preset("
|
| 63 |
model(input_data)
|
| 64 |
|
| 65 |
# Randomly initialized backbone with a custom config
|
|
@@ -70,7 +70,7 @@ model = keras_hub.models.VGGBackbone(
|
|
| 70 |
model(input_data)
|
| 71 |
|
| 72 |
# Use VGG for image classification task
|
| 73 |
-
model = keras_hub.models.ImageClassifier.from_preset("
|
| 74 |
|
| 75 |
# User Timm presets directly from HuggingFace
|
| 76 |
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/vgg11.tv_in1k')
|
|
|
|
| 36 |
input_data = np.ones(shape=(2, 224, 224, 3))
|
| 37 |
|
| 38 |
# Pretrained backbone
|
| 39 |
+
model = keras_hub.models.VGGBackbone.from_preset("vgg_16_imagenet")
|
| 40 |
model(input_data)
|
| 41 |
|
| 42 |
# Randomly initialized backbone with a custom config
|
|
|
|
| 47 |
model(input_data)
|
| 48 |
|
| 49 |
# Use VGG for image classification task
|
| 50 |
+
model = keras_hub.models.ImageClassifier.from_preset("vgg_16_imagenet")
|
| 51 |
|
| 52 |
# User Timm presets directly from HuggingFace
|
| 53 |
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/vgg11.tv_in1k')
|
|
|
|
| 59 |
input_data = np.ones(shape=(2, 224, 224, 3))
|
| 60 |
|
| 61 |
# Pretrained backbone
|
| 62 |
+
model = keras_hub.models.VGGBackbone.from_preset("hf://keras/vgg_16_imagenet")
|
| 63 |
model(input_data)
|
| 64 |
|
| 65 |
# Randomly initialized backbone with a custom config
|
|
|
|
| 70 |
model(input_data)
|
| 71 |
|
| 72 |
# Use VGG for image classification task
|
| 73 |
+
model = keras_hub.models.ImageClassifier.from_preset("hf://keras/vgg_16_imagenet")
|
| 74 |
|
| 75 |
# User Timm presets directly from HuggingFace
|
| 76 |
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/vgg11.tv_in1k')
|