Instructions to use cemanaral/brand-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use cemanaral/brand-detection 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://cemanaral/brand-detection") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from cemanaral/brand-detection: direct link, hf CLI and curl.
- Browser
- Download file 224 kB
-
https://huggingface.co/cemanaral/brand-detection/resolve/main/saved_model.pb
- Command line
-
hf download hf://cemanaral/brand-detection/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/cemanaral/brand-detection/resolve/main/saved_model.pb
224 kB
- Xet hash:
- 2dbcc6133d5a9a8b35cd766c7d5b0b23e1263fe6bde6cd31d43abb48bcafa445
- Size of remote file:
- 224 kB
- SHA256:
- e5d855366d932eb2575c97261fc28acee53447f3c2088f146a33f36e0ca3b1f9
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