Image caption generator using CNN as an encoder and RNN as an decoder.

Automatically generates captions for an image using Image processing and NLP. Model was trained on Flickr30K dataset.

pre-trained model and source code for generate description of images.

A neural image caption generator based on the "Show and Tell" paper.

Image Captioner Model

Giving short discription of Image using AI

Simple image caption generator built upon Xception net using CNN and LSTM.

COL774 Machine Learning Spring-2019-2020 IIT Delhi. Instructor - Prof. Parag Singla

Fabricating a Python application that generates a caption for a selected image. Involves the use of Deep Learning and NLP Frameworks in Tensorflow, Keras and NLTK modules for data processing and creation of deep learning models and their evaluation.

Fabricating a Python application that generates a caption for a selected image. Involves the use of Deep Learning and NLP Frameworks in Tensorflow, Keras and NLTK modules for data processing and creation of deep learning models and their evaluation.

This module generate proper caption for given image in bengali.

Inspired from the paper "Show Attend and Tell". This project's aim was to train a neural network which can provide descriptive text for a given image.

Image Caption Generator implemented using Tensorflow and Keras in a Python Jupyter Notebook. The goal is to describe the content of an image by using a CNN and RNN.

Порождение подписей к изображениям. Классификация изображений. Прогнозирование временных рядов

Immodal: Storytellor who never lies! Image caption generator + story generator powered by Cohere API

Generates textual description of any given image. Use both Natural Language Processing (NLP) and Computer Vision to generate captions. The idea implemented is to replace the encoder (RNN layer) in an encoder-decoder architecture with a deep Convolutional Neural Network (CNN) trained to classify objects in images.

Generates textual description of any given image. Use both Natural Language Processing (NLP) and Computer Vision to generate captions. The idea implemented is to replace the encoder (RNN layer) in an encoder-decoder architecture with a deep Convolutional Neural Network (CNN) trained to classify objects in images.