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--- |
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tags: |
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- image-to-text |
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- generic |
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library_name: generic |
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pipeline_tag: image-to-text |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-1.jpg |
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example_title: Kedis |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg |
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example_title: Cat in a Crate |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-3.jpg |
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example_title: Two Cats Chilling |
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license: cc0-1.0 |
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--- |
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## Tensorflow Keras Implementation of an Image Captioning Model with encoder-decoder network. 🌃🌅🎑 |
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This repo contains the models and the notebook [on Image captioning with visual attention](https://www.tensorflow.org/tutorials/text/image_captioning?hl=en). |
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Full credits to TensorFlow Team |
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## Background Information |
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This notebook implements TensorFlow Keras implementation on Image captioning with visual attention. |
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Given an image like the example below, your goal is to generate a caption such as "a surfer riding on a wave". |
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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. |
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The model architecture is similar to [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://arxiv.org/abs/1502.03044). |
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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. |
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