image-captioning with RNN

In this project, we implement a model to generate caption for flicker images using RNN. The goal of image captioning is to generate a textual description of an image that accurately reflects its content. In this approach, a pre-trained convolutional neural network (CNN) is used to extract visual features from the input image, and a language model based on RNNs, such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), is trained to generate a natural language description of the image. The RNN generates the caption word by word, conditioned on the previously generated words and the image features. This process continues until an end-of-sequence token is generated, indicating the completion of the caption. The quality of the generated captions depends on the quality of the visual features extracted by the CNN and the language model used to generate the captions. This approach has been shown to achieve state-of-the-art performance on various benchmark datasets, and it has the potential to be used in applications such as image search and image retrieval.