Image_Caption_Generation

Introduction

A reimplementation of Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge Paper link: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7505636

The Network Structure

Result

  0) a group of young men standing next to each other . (p=0.002236)
  1) a group of people standing next to each other . (p=0.001442)
  2) a group of young men standing next to each other on a field . (p=0.000307)

  0) a woman standing next to a red fire hydrant . (p=0.000278)
  1) a woman sitting on a bench with a cell phone . (p=0.000023)
  2) a woman standing next to a red fire hydrant (p=0.000021)

Dataset：MSCOCO、Flickr30k、Flickr8k

MSCOCO:http://cocodataset.org/

Download mscoco dataset (3 files to /Show_And_Tell/data/mscoco/raw-data path)：

Training set:

http://msvocds.blob.core.windows.net/coco2014/train2014.zip

Evaluation set:

http://msvocds.blob.core.windows.net/coco2014/val2014.zip

Caption set:

Http://msvocds.blob.core.windows.net/annotations-1-0-3/captions_train-val2014.zip

Download the pretrain model of InceptionV3

http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz

Train

Natural Language Toolkit (NLTK) in needed first! Run the following code to install ：

        import nltk
        nltk.download()

Step 1: Fix the CNN parameters and train the LSTM language model 500K CNN parameters: pre-trained good parameters in the ImageNet dataset Training split: one sentence n words -> n-1 group training sequence

Step 2: Fine-tuning CNN parameters, CNN & LSTM training 100K together

Demo

Prepare your own pictures and run the following python file：

https://github.com/HqWei/Image_Caption_Generation/blob/master/Image_caption_generation/run_inference.py