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A multilabel classification model that predicts the type of flower by its image (with an accuracy reaching 95 %) using basically Convolutional Neural Networks, transfer learning and flask for deployment. It was part of AI challenge organized by IEEE CIS Tunisia during the TSYP congress (2019)

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amiradridi/Type-of-flower-detection

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Type-of-flower-detection project

• Created a multilabel classification model that predicts the type of flower by its image (with an accuracy reaching 95 %) using basically Convolutional Neural Networks, transfer learning and flask for deployment.

• Won the 1st Place in AI challenge organized by IEEE CIS Tunisia during the TSYP congress (2019)

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A multilabel classification model that predicts the type of flower by its image (with an accuracy reaching 95 %) using basically Convolutional Neural Networks, transfer learning and flask for deployment. It was part of AI challenge organized by IEEE CIS Tunisia during the TSYP congress (2019)

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