CLIPxGPT Captioner is Image Captioning Model based on OpenAI's CLIP and GPT-2.

Automate Fashion Image Captioning using BLIP-2. Automatic generating descriptions of clothes on shopping websites, which can help customers without fashion knowledge to better understand the features (attributes, style, functionality etc.) of the items and increase online sales by enticing more customers.

📷 Deployed image captioning ML model using Flask and access via Flutter app

An Image captioning web application combines the power of React.js for front-end, Flask and Node.js for back-end, utilizing the MERN stack. Users can upload images and instantly receive automatic captions. Authenticated users have access to extra features like translating captions and text-to-speech functionality.

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.

Deep learning-based image captioning with Flickr8k dataset. Code includes data prep, model training, and a Streamlit app.

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

Image captioning project.

Image captioning using beam search heuristic on top of the encoder-decoder based architecture

Image captioning model with Resnet50 encoder and LSTM decoder

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.

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

This is a Deep Learning model which uses Computer Vision and NLP to generate captions for images.

This Streamlit app is designed for image captioning and tagging using the Google Gemini AI

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.

Successfully developed an image caption generation model which can precisely generate the text caption of any particular image based on a certain vocabulary of distinct words.

Deployed image captioning ML model using Flask and access via Flutter app

Blip 2 Captioning, Mass Captioning, Question Answering, and other tools.