URIE: Universal Image Enhancementfor Visual Recognition in the Wild
This is the implementation of the paper "URIE: Universal Image Enhancement for Visual Recognition in the Wild" by T. Son, J. Kang, N. Kim, S. Cho and S. Kwak. Implemented on Python 3.7 and PyTorch 1.3.1.
For more information, check our project website and the paper on arxiv.
Requirements
You can install dependencies using
pip install -r requirements.txt
Datasets
You need to manually configure following environment variables to run the experiments.
All validation csv contains fixed combination of image, corruption and severity to guarantee the same result.
To conduct validation, you may need to change home folder path in each csv files given.
# DATA PATHS
export IMAGENET_ROOT=PATH_TO_IMAGENET
export IMAGENET_C_ROOT=PATH_TO_IMAGENET_C
# URIE VALIDATION
## ILSVRC VALIDATION
export IMAGENET_CLN_TNG_CSV=PROJECT_PATH/imagenet_dataset/imagenet_cln_train.csv
export IMAGENET_CLN_VAL_CSV=PROJECT_PATH/imagenet_dataset/imagenet_cln_val.csv
export IMAGENET_TNG_VAL_CSV=PROJECT_PATH/imagenet_dataset/imagenet_tng_tsfrm_validation.csv
export IMAGENET_VAL_VAL_CSV=PROJECT_PATH/imagenet_dataset/imagenet_val_tsfrm_validation.csv
## CUB VALIDATION
export CUB_IMAGE=PATH_TO_CUB
export DISTORTED_CUB_IMAGE=PATH_TO_CUB_C
export CUB_TNG_LABEL=PROJECT_PATH/datasets/eval_set/label_train_cub200_2011.csv
export CUB_VAL_LABEL=PROJECT_PATH/datasets/eval_set/label_val_cub200_2011.csv
export CUB_TNG_TRAIN_VAL=PROJECT_PATH/datasets/eval_set/tng_tsfrm_validation.csv
export CUB_TNG_TEST_VAL=PROJECT_PATH/datasets/eval_set/val_tsfrm_validation.csv
ILSVRC2012 Dataset
You can download the dataset from here and use it for training.
CUB dataset
You can download the original Caltech-UCSD Birds-200-2011 dataset from here, and corrupted version of CUB dataset from here.
Training
Training URIE with the proposed method on ILSVRC2012 dataset
python train_urie.py --batch_size BATCH_SIZE \
--cuda \
--test_batch_size BATCH_SIZE \
--epochs 60 \
--lr 0.0001 \
--seed 5000 \
--desc DESCRIPTION \
--save SAVE_PATH \
--load_classifier \
--dataset ilsvrc \
--backbone r50 \
--multi
Since training on ILSVRC dataset takes too long, you can train / test the model with cub dataset with following command.
python train_urie.py --batch_size BATCH_SIZE \
--cuda \
--test_batch_size BATCH_SIZE \
--epochs 60 \
--lr 0.0001 \
--seed 5000 \
--desc DESCRIPTION \
--save SAVE_PATH \
--load_classifier \
--dataset cub \
--backbone r50 \
--multi
Validation
You may use our pretrained model to validate or compare the results.
Classification
python inference.py --srcnn_pretrained_path PROJECT_PATH/ECCV_MODELS/ECCV_SKUNET_OURS.ckpt.pt \
--dataset DATASET \
--test_batch_size 32 \
--enhancer ours \
--recog r50
Detection
We have conducted object detection experiments using the codes from github.
You may compare the performance with the same evaluation code with attaching our model (or yours) in front of the detection model.
For valid comparison, you need to preprocess your data with mean and standard deviation.
Semantic Segmentation
We have conducted semantic segmentation experiments using the codes from github.
For backbone segmentation network, please you pretrained deeplabv3 on pytorch. You may compare the performance with the same evaluation code with attaching our model (or yours) in front of the segmentation model.
For valid comparison, you need to preprocess your data with mean and standard deviation.
Image Comparison
If you want just simple before & output image comparison, you can use render.py as following command.
python render.py IMAGE_FILE_PATH
It runs given image file through pretrained URIE model, and saves enhanced output image comparison in current project file as "output.jpg".
BibTeX
If you use this code for your research, please consider citing:
@InProceedings{son2020urie,
title={URIE: Universal Image Enhancement for Visual Recognition in the Wild},
author={Son, Taeyoung and Kang, Juwon and Kim, Namyup and Cho, Sunghyun and Kwak, Suha},
booktitle={ECCV},
year={2020}
}
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