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Image Captioning

Image captioning pet project, based on Udacity's Computer Vision Nanodegree project.

How to run

After downloading coco dataset and installing the requirements, you can run python3 run.py -h to display the available options.

Training a model

python3 run.py --train --epochs 10 --batch-size 16 --log-file training_logs.txt

Visualizing results on coco test

If you have an encoder and decoder saved using torch save function: python3 run.py --inference-coco --encoder-file encoder.pkl --decoder-file decoder.pkl

Requirements

Run pip3 install -r requirements.txt to install the required python packages

This repo has been tested on Ubuntu 16.04, 18.04 and Elementary OS 5.0 Juno.

If you want to use GPU, please be sure that you have CUDA installed.

Example

Prediction: a display case filled with lots of donuts .

COCO dataset

  • First, clone the coco api repository into /opt directory
  • Then, go to coco website and download the 2014 test and train images.
  • Create an image folder: mkdir /opt/cocoapi/images
  • Unzip the train and test images into /opt/cocoapi/images.
  • Unzip the train and test annotations into /opt/cocoapi/

Your /opt/cocoapi directory should look like:

├── annotations
│   ├── captions_train2014.json
│   ├── captions_val2014.json
│   ├── image_info_test2014.json
│   ├── instances_train2014.json
│   ├── instances_val2014.json
│   ├── person_keypoints_train2014.json
│   └── person_keypoints_val2014.json
├── common
│   ├── gason.cpp
│   ├── gason.h
│   ├── maskApi.c
│   └── maskApi.h
├── images
│   ├── test2014
│   └── train2014
├── license.txt
├── LuaAPI
│   ├── CocoApi.lua
│   ├── cocoDemo.lua
│   ├── env.lua
│   ├── init.lua
│   ├── MaskApi.lua
│   └── rocks
├── MatlabAPI
│   ├── CocoApi.m
│   ├── cocoDemo.m
│   ├── CocoEval.m
│   ├── CocoUtils.m
│   ├── evalDemo.m
│   ├── gason.m
│   ├── MaskApi.m
│   └── private
├── PythonAPI
│   ├── Makefile
│   ├── pycocoDemo.ipynb
│   ├── pycocoEvalDemo.ipynb
│   ├── pycocotools
│   └── setup.py
├── README.txt
└── results
    ├── captions_val2014_fakecap_results.json
    ├── instances_val2014_fakebbox100_results.json
    ├── instances_val2014_fakesegm100_results.json
    ├── person_keypoints_val2014_fakekeypoints100_results.json
    └── val2014_fake_eval_res.txt

TODO:

  • Add resume training option
  • Add video processing support