Look-into-object-project. Blog link

This is unofficial pytorch implementation of Look-into-Object: Self-supervised Structure Modeling for Object Recognition CVPR2020).
This repository is created for the Visual Media class of UTokyo (as my class report).
Check the blog for details.

Getting started

This work was tested with PyTorch 1.2.0, torchvision 0.4.0, CUDA 10.0, python 3.6 and Ubuntu 16.04.
requirements: numpy, pillow, torchvision, tqdm, pretrainedmodels, pandas

pip install numpy pillow torchvision tqdm pretrainedmodels pandas 

Follow DCL repository's instruction to download and prepare datasets; CUB, STCAR(=CAR), and AIR.

Train

Train Look Into Object (LIO) module

CUDA_VISIBLE_DEVICES=0 python train.py --data CUB --mo LIO --exp_name LIO
CUDA_VISIBLE_DEVICES=1 python train.py --data STCAR --mo LIO --exp_name LIO
CUDA_VISIBLE_DEVICES=2 python train.py --data AIR --mo LIO --exp_name LIO

Train Object-Extent Learning (OEL) module

CUDA_VISIBLE_DEVICES=0 python train.py --data CUB --mo OEL --exp_name OEL
CUDA_VISIBLE_DEVICES=1 python train.py --data STCAR --mo OEL --exp_name OEL
CUDA_VISIBLE_DEVICES=2 python train.py --data AIR --mo OEL --exp_name OEL

Train Spatial Context Learning (SCL) module

CUDA_VISIBLE_DEVICES=0 python train.py --data CUB --mo SCL --exp_name SCL
CUDA_VISIBLE_DEVICES=1 python train.py --data STCAR --mo SCL --exp_name SCL
CUDA_VISIBLE_DEVICES=2 python train.py --data AIR --mo SCL --exp_name SCL

Train baseline (ResNet50)

CUDA_VISIBLE_DEVICES=0 python train.py --data CUB --exp_name baseline
CUDA_VISIBLE_DEVICES=1 python train.py --data STCAR --exp_name baseline
CUDA_VISIBLE_DEVICES=2 python train.py --data AIR --exp_name baseline

Test

CUDA_VISIBLE_DEVICES=0 python test.py --data CUB --save saved_models/[path-to-trained_model]
CUDA_VISIBLE_DEVICES=1 python test.py --data STCAR --save saved_models/[path-to-trained_model]
CUDA_VISIBLE_DEVICES=2 python test.py --data AIR --save saved_models/[path-to-trained_model]

Acknowledgement

We borrowed some codes from DCL repository.