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