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TensorFlow Implementation of "Show, Attend and Tell"
| Date | Stars |
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| 2026-07-24 | 905 |
| 2026-07-25 | 905 |
| 2026-07-28 | 905 |
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# Show, Attend and Tell <b> Update (December 2, 2016)</b> TensorFlow implementation of [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](http://arxiv.org/abs/1502.03044) which introduces an attention based image caption generator. The model changes its attention to the relevant part of the image while it generates each word. <br/>  <br/> ## References Author's theano code: https://github.com/kelvinxu/arctic-captions Another tensorflow implementation: https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow <br/> ## Getting Started ### Prerequisites First, clone this repo and [pycocoevalcap](https://github.com/tylin/coco-caption.git) in same directory. ```bash $ git clone https://github.com/yunjey/show-attend-and-tell-tensorflow.git $ git clone https://github.com/tylin/coco-caption.git ``` This code is written in Python2.7 and requires [TensorFlow 1.2](https://www.tensorflow.org/versions/r1.2/install/install_linux). In addition, you need to install a few more packages to process [MSCOCO data set](http://mscoco.org/home/). I have provided a script to download the <i>MSCOCO image dataset</i> and [VGGNet19 model](http://www.vlfeat.org/matconvnet/pretrained/). Downloading the data may take several hours depending on the network speed. Run commands below then the images will be downloaded in `image/` directory and <i>VGGNet19 model</i> will be downloaded in `data/` directory. ```bash $ cd show-attend-and-tell-tensorflow $ pip install -r requirements.txt $ chmod +x ./download.sh $ ./download.sh ``` For feeding the image to the <i>VGGNet</i>, you should resize the <i>MSCOCO image dataset</i> to the fixed size of 224x224. Run command below then resized images will be stored in `image/train2014_resized/` and `image/val2014_resized/` directory. ```bash $ python resize.py ``` Before training the model, you have to preprocess the <i>MSCOCO caption dataset</i>. To generate caption dataset and image feature vectors, run command below. ```bash $ python prepro.py ``` <br> ### Train the model To train the image captioning model, run command below. ```bash $ python train.py ``` <br> ### (optional) Tensorboard visualization I have provided a tensorboard visualization for real-time debugging. Open the new terminal, run command below and open `http://localhost:6005/` into your web browser. ```bash $ tensorboard --logdir='./log' --port=6005 ``` <br> ### Evaluate the model To generate captions, visualize attention weights and evaluate the model, please see `evaluate_model.ipynb`. <br/> ## Results <br/> #### Training data ##### (1) Generated caption: A plane flying in the sky with a landing gear down.  ##### (2) Generated caption: A giraffe and two zebra standing in the field.  #### Validation data ##### (1) Generated caption: A large elephant standing in a dry grass field.  ##### (2) Generated caption: A baby elephant standing on top of a dirt field.  #### Test data ##### (1) Generated caption: A plane flying over a body of water.  ##### (2) Generated caption: A zebra standing in the grass near a tree. 
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Yunjey Choi · NAVER AI Lab · South Korea
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b2960b31b274c31e, topic:image-captioning, readme:image captioning
matched fp:b2960b31b274c31e, topic:tensorflow