Instructions to use SBB/eynollah-image-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use SBB/eynollah-image-extraction with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("SBB/eynollah-image-extraction") - Keras
How to use SBB/eynollah-image-extraction with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://SBB/eynollah-image-extraction") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be85e99e52c644e69b545e14c3f881616f192ca40cf663fa17dfb2ea51a550ee
- Size of remote file:
- 148 MB
- SHA256:
- 092a21fde8fd64e7cb37e04c83ccb958ec8e0742766c2aa529ef824aa0bcda0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.