Instructions to use DataScienceProject/CNN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DataScienceProject/CNN 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/CNN") - Notebooks
- Google Colab
- Kaggle
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README.md
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This project provides a Convolutional Neural Network (CNN) model for classifying images as either 'real art' or 'fake art'.
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CNN is a type of deep learning model specifically designed to process and analyze visual data by applying convolutional layers that automatically detect patterns and features in images.
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Our goal is to accurately classify the source of the image with at least 85% accuracy and achieve at least 80% in the
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***Installation instructions***
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This project provides a Convolutional Neural Network (CNN) model for classifying images as either 'real art' or 'fake art'.
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CNN is a type of deep learning model specifically designed to process and analyze visual data by applying convolutional layers that automatically detect patterns and features in images.
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Our goal is to accurately classify the source of the image with at least 85% accuracy and achieve at least 80% in the recall test.
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***Installation instructions***
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