Instructions to use Bhavi23/EfficientNet_B0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Bhavi23/EfficientNet_B0 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Bhavi23/EfficientNet_B0") - Notebooks
- Google Colab
- Kaggle
Upload efficientnet_checkpoint.weights.h5
Browse files
efficientnet_checkpoint.weights.h5
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0ab06616182a44ab16be53cad1f562f67d7068db934ac474ad5b41d520c87d93
|
| 3 |
+
size 102071984
|