https://github.com/zsdonghao/image-captioning

TensorFlow (TensorLayer) Implementation of Image Captioning
https://github.com/zsdonghao/image-captioning

deep-learning image-captioning tensorflow tensorlayer

Last synced: 13 days ago
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TensorFlow (TensorLayer) Implementation of Image Captioning

• Host: GitHub
• URL: https://github.com/zsdonghao/image-captioning
• Owner: zsdonghao
• Created: 2016-10-07T15:03:31.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2022-10-04T18:27:11.000Z (about 2 years ago)
• Last Synced: 2024-10-23T07:51:29.490Z (22 days ago)
• Topics: deep-learning, image-captioning, tensorflow, tensorlayer
• Language: Python
• Homepage:
• Size: 199 KB
• Stars: 115
• Watchers: 7
• Forks: 55
• Open Issues: 7
• Metadata Files:
  • Readme: README.md

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README

        
# Image Captioning

We reimplemented the complicated [Google' Image Captioning](https://github.com/tensorflow/models/tree/master/im2txt) model by simple [TensorLayer](https://github.com/zsdonghao/tensorlayer) APIs.

This script run well under Python2 or 3 and TensorFlow 0.10 or 0.11.

### 1. Prepare MSCOCO data and Inception model

Before you run the scripts, you need to follow Google's [setup guide]((https://github.com/tensorflow/models/tree/master/im2txt)), and setup the model, ckpt and data directories in *.py.

- Creat a ``data`` folder.

- Download and Preprocessing MSCOCO Data [click here](https://github.com/tensorflow/models/tree/master/research/im2txt)

- Download the Inception_V3 CKPT [click here](https://github.com/tensorflow/models/tree/master/research/slim)

### 2. Train the model

- via ``train.py``

### 3. Evaluate the model

- via ``evaluate.py``

### 4. Generate captions by given image and model

- via ``run_inference.py``

### 5. Evaluation

- [tylin/coco-caption](https://github.com/tylin/coco-caption/blob/master/cocoEvalCapDemo.ipynb)