Instructions to use atuml/FishNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use atuml/FishNet with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://atuml/FishNet") - Notebooks
- Google Colab
- Kaggle
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Download README.md from atuml/FishNet: direct link, hf CLI and curl.
- Browser
- Download file 88 Bytes
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https://huggingface.co/atuml/FishNet/resolve/main/README.md
- Command line
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hf download hf://atuml/FishNet/README.md
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curl -L -o README.md https://huggingface.co/atuml/FishNet/resolve/main/README.md
88 Bytes
metadata
metrics:
- accuracy
- mae
library_name: keras
pipeline_tag: image-classification