Instructions to use itshemantkmr/2D_Shape_Image_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use itshemantkmr/2D_Shape_Image_Classification 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://itshemantkmr/2D_Shape_Image_Classification") - Notebooks
- Google Colab
- Kaggle
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Download README.md from itshemantkmr/2D_Shape_Image_Classification: direct link, hf CLI and curl.
- Browser
- Download file 186 Bytes
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https://huggingface.co/itshemantkmr/2D_Shape_Image_Classification/resolve/main/README.md
- Command line
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hf download hf://itshemantkmr/2D_Shape_Image_Classification/README.md
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curl -L -o README.md https://huggingface.co/itshemantkmr/2D_Shape_Image_Classification/resolve/main/README.md
186 Bytes
| license: apache-2.0 | |
| language: | |
| - en | |
| metrics: | |
| - accuracy | |
| base_model: google/mobilenet_v2_1.0_224 | |
| pipeline_tag: image-classification | |
| datasets: | |
| - itshemantkmr/2D_Shape_Image_Datasets | |