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Deliverable 1Multi-Class Classification and Detection on Emoji DatasetGoal: To perform multi-class Classification and Detection on self-generated emoji dataset using Keras and Open CV Aim: To get acquainted with the concepts of machine learning and artificial intelligence Conclusion: Performed emoji detection through OpenCV and classification through Keras. Low accuracy due to imbalance of images present under each emoji class. This deliverable has been divided into 2 parts. Deliverable 1.1: Object Detection In this part, object detection is performed with the help of OpenCV. The number of emojis present in an image is detected with the help of CV2 Hough Circles.Emoji Detection Code Here are some of the results: Detecting single emoji in an image Detecting two emojis in an image Deliverable 1.2: Object Classification In this part, we classify all the emojis into best predicted classes present in an image. Here all the images will contain upto 3 emojis. To achieve this a simple neural net is designed using Keras to perform multi-class classification. The accuracy of the model is not at its best level due to some inconsistencies in the dataset.Emoji Classification Code Model Architecture Here are some of the results: Correct Classification(Predicted Classes) Incorrect Classification(Predicted Classes) The accuracy of the model is poor due to inconsistent data present under each class. Dataset Emojis dataset was generated using PIL library. 5 kinds of emotions were chosen and upto 3 of them were written to the image. Random emojis size were placed randomly in any position in the image. A total of 1000 such images were created. This dataset was further divided into training and test dataset.Generation of Dataset Code Image consisting of 1 emoji Image consisting of 2 emojis Image consisting of 3 emojis |