There are 1 repository under cnn-rnn topic.
A minimum unofficial implementation of the "A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement" (CRN) using PyTorch
This repositary contain all my exercises and projects of Udacity Computer Vision Nanodegree Program
Contains additional materials for two keras.io blog posts.
Sequence-to-sequence model implementations including RNN, CNN, Attention, and Transformers using PyTorch
16 projects in the framework of Computer Vision algorithms: 16 projects in the framework of Computer Vision algorithms: CNN, RNN, LSTM, Facial KeyPoints, Image Captioning, SLAM, Edge Detectors, Day Night Classifier, etc.
Caption generation through a CNN-RNN model further to be converted to speech using a text to speech library for a visually challenged person for understanding the content of an image in the form of speech.
This project aims to assist visually impaired individuals by providing a solution to convert images into spoken language. Leveraging deep learning and natural language processing, the system processes images, generates descriptive captions, and converts these captions into audio output.
This project builds a video classification model using CNNs for spatial feature extraction and RNNs for temporal sequence modeling. Utilizing the UCF101 dataset, it covers data preprocessing, feature extraction, model training, and evaluation, providing a comprehensive approach to action recognition in videos.
A WebApp that Generates Caption for Images using CNN-RNN Architecture
Tuning, training, and transfer learning CRNN models for handwritten text words.
scene text detection
A Deep Learning-based approach to classify human gestures for smart appliances.
Image Captioning using CNN-RNN architecture made with :heart: in Pytorch. Do :star2: he repo if you find it useful :rocket:
Built a CNN-RNN neural network architecture to automatically generate captions from images describing that image.
To develop gesture recognition feature for smart TV which help user control the TV without using remote.
A neural network architecture that automatically generate captions from images.
Transform TV control with Gesture Recognition! Enable intuitive interaction with smart TVs using gestures built using Conv3D, CNN & RNN
PyTorch Re-implementation of RNATracker Model.
AGENT is a mobile app I developed as a requirement for completing my special problem in UPLB
This GitHub repository contains the implementation of a deep learning model capable of generating captions for images in the form of speech.
We want to classify songs to their music genre by using spectograms, i.e. plots showing frequencies in a sequential way. To deal with that, we started by using CNN, thus treating spectograms as standard images. We managed to include also the sequential information of frequencies by stacking RNN and CNN layers.
Develop a cool feature in the smart-TV that can recognise five different gestures performed by the user which will help users control the TV without using a remote.
This project showcase the usage of Neural Network algorithms to develop a feature for smart TVs. The implementation is able to detect 5 different gestures performed by the user, allowing them to control the TV without a remote.
Developed an image captioning system using the BLIP model to generate detailed, context-aware captions. Achieved an average BLEU score of 0.72, providing rich descriptions that enhance accessibility and inclusivity.
Image Captioning Generator Keras