Instructions to use DataScienceProject/Resnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DataScienceProject/Resnet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://DataScienceProject/Resnet") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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datasets:
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- DataScienceProject/Art_Images_Ai_And_Real_
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---
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**Real art vs AI-Generated art image classification**
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license: apache-2.0
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datasets:
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- DataScienceProject/Art_Images_Ai_And_Real_
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pipeline_tag: image-classification
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---
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**Real art vs AI-Generated art image classification**
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