A Keras Implementation for the DeepMIML Network

This is the official clone for the implementation of DeepMIML Network.(The University's webserver is unstable sometimes, therefore we put the official clone here at github)

It is a deep model for multi-instance multi-label learning.

Package Official Website: http://lamda.nju.edu.cn/code_deepmiml.ashx

This package is provided "AS IS" and free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (zhouzh@lamda.nju.edu.cn).

Reference: [1] J. Feng and Z.-H. Zhou. "DeepMIML network" In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

File Structure

• demo/
  • The demo scipts
• lib/deepmiml
  • The DeepMIML implementation and visualization tools
• lib/pycocotools
  • The Official COCO API
• lib/cocodemo
  • An implementation of the COCO Datalayer used for keras to training on the COCO dataset

Environment Requirements

The package is developed in python 2.7 with Anaconda, higher version of python hasn't been tested.

To install the requirement packages. Run the following commands

conda install opencv
pip install -r requirements.txt

Add/Modify the following two config items in ~/.keras/kears.json.

"image_dim_ordering": "th"
"backend": theano

Compile the COCO API

cd lib
make

COCO Data Experiment

Download the dataset

mkdir -p data/coco
cd data/coco
wegt http://msvocds.blob.core.windows.net/coco2014/train2014.zip
wget http://msvocds.blob.core.windows.net/coco2014/val2014.zip
wget http://msvocds.blob.core.windows.net/annotations-1-0-3/instances_train-val2014.zip
unzip train2014.zip
unzip val2014.zip
unzip instances_train-val2014.zip

Download the pretraind VGG16 Model

Goto the webpage: https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3

Download the the vgg16_weights.h5 posted by the author.

Put the model file into models/imagenet/vgg

Train the DeepMIML network

This script will train the DeepMIML with 10 epochs, and save the model file in outputs/coco_2014_train/miml_vgg_16

Modify demo/demo_train_miml_vgg.py to change hyper-parameters for training configurations.

python demo/demo_train_miml_vgg.py

Visualization

If you want to Visualize the MIML analysis for the n-th image in the test set, run the command below: (here n is the first 2 test data)

python demo/demo_vis_miml_vgg.py --model outputs/coco_2014_train/miml_vgg_16 --data 1
python demo/demo_vis_miml_vgg.py --model outputs/coco_2014_train/miml_vgg_16 --data 2

It means, use the model in outputs/coco_2014_train/miml_vgg_16 (--model),

and test it on the test set. (--data N means use the N-th image in the test set.)

It will produce 2 visualization per test sample: (as illustrated in the original paper)

The result for the first image:

The result for the second image:

Result Evaluation

python demo/demo_test_miml_vgg.py --model outputs/coco_2014_train/miml_vgg_16