readme: ---
  tags:
  - image-to-text
  - generic
  library_name: generic
  pipeline_tag: image-to-text
  widget:
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-1.jpg
    example_title: Kedis
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg
    example_title: Cat in a Crate
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-3.jpg
    example_title: Two Cats Chilling
  license: cc0-1.0
  ---
  ## Tensorflow Keras Implementation of an Image Captioning Model with encoder-decoder network. 🌃🌅🎑
  
  This repo contains the models and the notebook [on Image captioning with visual attention](https://www.tensorflow.org/tutorials/text/image_captioning?hl=en).
  
  Full credits to TensorFlow Team
  
  ## Background Information
  This notebook implements TensorFlow Keras implementation on Image captioning with visual attention.
  Given an image like the example below, your goal is to generate a caption such as "a surfer riding on a wave".
  ![image](https://www.tensorflow.org/images/surf.jpg)
  To accomplish this, you'll use an attention-based model, which enables us to see what parts of the image the model focuses on as it generates a caption.
  ![attention](https://www.tensorflow.org/images/imcap_prediction.png)
  The model architecture is similar to [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://arxiv.org/abs/1502.03044).
  
  This notebook is an end-to-end example. When you run the notebook, it downloads the [MS-COCO](https://cocodataset.org/#home) dataset, preprocesses and caches a subset of images using Inception V3, trains an encoder-decoder model, and generates captions on new images using the trained model.
  