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The provided code demonstrates transfer learning by adapting a model trained using synthetic data to classify circles, squares, and triangles to classify new shapes like stars and pentagons. By fine-tuning a pre-trained model originally designed for a different task, the repository showcases how to efficiently adapt a model to a new domain.
A dog breed identification model specializing in precise classification from images, leveraging advanced transfer learning techniques with TensorFlow, Keras, and GPU acceleration
An end-to-end multi-class image classification system(web app) that classifies 101 classes of food. I'll be implementing the popular CNN architecture while utilizing the full power of transfer learning to extract features and fine-tune layers. I'll also build an interactive UI using react-js and deploy the system.
Photo sharing and photo storage services like to have location data for each photo that is uploaded. With the location data, these services can build advanced features, such as automatic suggestion of relevant tags or automatic photo organization, which help provide a compelling user experience.
Deep neural network model combining audio signal processing and pre-trained audio CNN achieved 90.1% adjusted accuracy (27.6% improvement) for classifying audio recording environment.
It is a autonomous robot equipped with sensors and cameras with deep learning algorithms to monitor and maintain crop health and act as an aid to farmers and huge estate or nursery owners
Transfer Learning using InceptionResNetV2 to the Augmented Neuroimage Data for the Autism Spectrum Disorder (ASD) Classification, using ABIDE I dataset.
Convolutional Neural Networks capable of classifying Normal vs. Pneumonia frontal chest radiograph (a set of 32 images in 8 seconds) using Transfer Learning with ResNet50