imagecaptioning

Multi-Modal learning toolkit based on PaddlePaddle and PyTorch, supporting multiple applications such as multi-modal classification, cross-modal retrieval and image caption.

Image to LaTeX (Seq2seq + Attention with Beam Search) - Tensorflow

Pytorch implemention of Deep CNN Encoder + LSTM Decoder with Attention for Image to Latex

Image captioning ready-to-go inference: show and tell model compatible with Tensorflow r1.9

Code for GHA (ACCV2018)

Repo for Implementing Research Papers & Projects related to Machine Learning

ImageCaptioning improved with an attention mechanism. Also a PyQt5 application

Some interesting applications of RNN, e.g. char rnn (pomes generation), seq2seq (machine translation), image captioning (NIC)

An implementation of the paper "Context-aware Captions from Context-agnostic Supervision"

AI Poet who looks at the images and writes poems Web service.

ImgCap is an image captioning model designed to automatically generate descriptive captions for images. It has two versions CNN + LSTM model and CNN + LSTM + Attention mechanism model.

Here are all my code files of Advanced AI/ML architectures built from scratch using Pytorch.

Generating caption from medical image like xray

Image captioning with a CNN (InceptionV3) encoder and LSTM decoder, trained on Flickr8k — includes training/inference scripts and a Flask web demo for uploading an image and generating a caption.

Five practical AI applications spanning RAG-based document Q&A, BlenderBot conversation, BLIP image captioning, Whisper transcription, and a Watson STT/TTS voice-assistant workflow.

This project implements an image captioning model using a CNN-LSTM architecture. The model takes an image as input and generates a descriptive caption using natural language processing techniques

VisionVerse is a versatile tool that integrates image captioning, pre-trained image classification, and text-to-image generation.

A versatile app that converts images into short stories and lifelike audio locally. It combines Hugging Face's image captioning, Groq's story generation, and Parler TTS for local text-to-speech synthesis. Ideal for AI-driven projects with fast, reliable on-device TTS.