Image Classification
Keras
LiteRT
English
Research
Education
Science and Technology
Artificial Intelligence
Computer Science
Computer Vision
CNN
Image
Keras
TensorFlow
Python
Instructions to use me-aas/EinsteinNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use me-aas/EinsteinNet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://me-aas/EinsteinNet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3fc25537976b858b8f996d3e1c395456afa506d35d9f51a50f772e1b4945d80
- Size of remote file:
- 2.66 MB
- SHA256:
- 7d34aabb2a6109aab9d8874876420c2c1ef2c4f31fa575d8e6eb2eeed363a970
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.