Image captioning typically relies on a combination of Convolutional Neural Networks (CNNs) for image feature extraction and Recurrent Neural Networks (RNNs), such as LSTMs or GRUs, to generate descriptive text based on those features. More recently, transformer-based models have also been used for better contextual understanding.
Image captioning typically relies on a combination of Convolutional Neural Networks (CNNs) for image feature extraction and Recurrent Neural Networks (RNNs), such as LSTMs or GRUs, to generate descriptive text based on those features. More recently, transformer-based models have also been used for better contextual understanding. CNN Encoder – Extracts features from the input image. RNN/T Transformer Decoder – Translates the CNN output into a human-readable sentence. Flask API – Exposes endpoints to receive images and return captions. MQTT Broker – Could be used to publish or subscribe to events/images in a real-time pipeline. InfluxDB & Grafana – Possibly storing metadata or performance metrics related to captioning for monitoring and analytics.