Awesome
MIST: Multiple Instance Spatial Transformer Network
Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi
This repository contains training and inference code for MIST: Multiple Instance Spatial Transformer Network.
Installation
This code is implemented based on PyTorch. A conda environment is provided with all the dependencies:
conda env create -f system/conda_mist.yaml
Pretrained models and datasets
Two pretrained models are provided for MNIST dataset and trimmed Pascal+COCO dataset respectively. Models download path:
mkdir pretrained_models
wget https://www.cs.ubc.ca/research/kmyi_data/files/2021/mist/mnist_best_models -P ./pretrained_models/
wget https://www.cs.ubc.ca/research/kmyi_data/files/2021/mist/pascal_coco_best_models -P ./pretrained_models/
Dataset download path:
mkdir dataset
wget https://www.cs.ubc.ca/research/kmyi_data/files/2021/mist/mnist_hard.zip -P ./dataset/
wget https://www.cs.ubc.ca/research/kmyi_data/files/2021/mist/VOC_pascal_coco_v2.zip -P ./dataset/
unzip ./dataset/mnist_hard.zip -d ./dataset/
unzip ./dataset/VOC_pascal_coco_v2.zip -d ./dataset/
Inference
Following commands will run pretrained model on test set. Visualization can be found in './test_results'
python mist_test.py --path_json='json/pascal.json'
python mist_test.py --path_json='json/mnist.json'
Citation
@inproceedings{angles2021mist,
title={MIST: Multiple Instance Spatial Transformer Networks},
author={Baptiste Angles*, Yuhe Jin*, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2021}
}