Noxtrah/ECG-Signal-Classification

This project classifies ECG Signal as AF(Atrial Fibrillation) or Non-AF(All other rhythms).This project consist of 2 different models. A custom cnn and a transfer learning model. These model are doing the same thing with different approaches.

Language: Python - Size: 4.27 MB - Last synced at: 23 days ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

SumaiyaTarannumNoor/Age-Estimation-using-Classification

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SumaiyaTarannumNoor/A-Deep-Learning-Approach-to-Mango-Leaf-Disease-Detection

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DasariJayanth/Sign-to-Speech

sign-to-speech

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frncscp/ptctrn

Legacy repo for the Artificial Intelligence capable of patacón recognition (Now on HuggingFace)

Language: Python - Size: 86.7 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 11 - Forks: 1

SheezaShabbir/MultiTaskLearning-Multi-label-Classification-using-CNN-

Training a new classification network which should tell the identity label of an image and also be able to classify: ● Gender as Male/Female ● Wearing Glasses/ Not Wearing Glasses ● Smiling/ Not Smiling ● Wearing hat/ not wearing hat ● young/old. As we need 5 outputs from a single input. The custom CNN model consists of NN architecture with 5 heads return my the model. My model performance in Multilabel classification task is totally remarkable.

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SheezaShabbir/FaceVerification-using-CNN-Custom-model

Implementing a model that can verify if two images belongs to same personality or not. Answer the question "Is this the claimed person?" It is a 1:1 matching problem i.e. given a face your task is to compare the candidate face to another and verify whether it is a match or not. My custom CNN model has achieved marvelous performance on the dataset.

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AnaghaVinayakKamat/Custom-CNN-Image-Classification

Our novel approach is dedicated to refining image classification using customized CNN Kernel. The primary objective is to enhance algorithmic performance, bolstering adaptability and feature extraction capabilities.

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SalehAhmedShafin/Pytorch-Custom-CNN-model

The architecture is a deep neural network for image processing, composed of convolutional layers capturing features, followed by pooling and fully connected layers. It uses ReLU activation, dropout for regularization, and ends with a linear output layer for classification.

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