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Python code showing the usage of different feature extractors such as VGG16, VGG19, ResNet50 and InceptionV3 and using them on different prediction algorithms such as Logistic regression, SVM, Naive Bayes, Decision tree and Random forest to analyze the result and see the efficacy of reinforcement learning.
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Presented in 2021 IEEE International Conference on Robotics, Automation and Artificial Intelligence (RAAI), Hong Kong.
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the dataset used was scraped from google.
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the code used to train the model and do the predictive analysis is here
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You can find the research paper here
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Google colab was used to run the code.
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Python code showing the usage of different feature extractors such as VGG16, VGG19, ResNet50 and InceptionV3 and using them on different prediction algorithms such as Logistic regression, SVM, Naive Bayes, Decision tree and Random forest to analyze the result and see the efficacy of reinforcement learning.
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pranav1601/Forest-fire-detection-using-classifiers-and-transfer-learning
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Python code showing the usage of different feature extractors such as VGG16, VGG19, ResNet50 and InceptionV3 and using them on different prediction algorithms such as Logistic regression, SVM, Naive Bayes, Decision tree and Random forest to analyze the result and see the efficacy of reinforcement learning.
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