Implementation for paper "STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement" (ICCV 2021).

Overview

STAR-pytorch

Implementation for paper "STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement" (ICCV 2021).

CVF (pdf)

STAR-DCE

The pytorch implementation of low light enhancement with STAR on Adobe-MIT FiveK dataset. You can find it in STAR-DCE directory. Here we adopt the pipleline of Zero-DCE ( paper | code ), just replacing the CNN backbone with STAR. In Zero-DCE, for each image the network will regress a group of curves, which will then applied on the source image iteratively. You can find more details in the original repo Zero-DCE.

Requirements

  • numpy
  • einops
  • torch
  • torchvision
  • opencv

Datesets

We provide download links for Adobe-MIT FiveK datasets we used ( train | test ). Please note that we adopt the test set splited by DeepUPE for fair comparison.

Training DCE models

To train a original STAR-DCE model,

cd STAR-DCE
python train_dce.py 
  --lowlight_images_path "dir-to-your-training-set" \
  --parallel True \
  --snapshots_folder snapshots/STAR-ori \
  --lr 0.001 \
  --num_epochs 100 \
  --lr_type cos \
  --train_batch_size 32 \
  --model STAR-DCE-Ori \
  --snapshot_iter 10 \
  --num_workers 32 \

To train the baseline CNN-based DCE-Net (w\ or w\o Pooling),

cd STAR-DCE
python train_dce.py 
  --lowlight_images_path "dir-to-your-training-set" \
  --parallel True \
  --snapshots_folder snapshots/DCE \
  --lr 0.001 \
  --num_epochs 100 \
  --lr_type cos \
  --train_batch_size 32 \
  --model DCE-Net \
  --snapshot_iter 10 \
  --num_workers 32 \

or

cd STAR-DCE
python train_dce.py 
  --lowlight_images_path "dir-to-your-training-set" \
  --parallel True \
  --snapshots_folder snapshots/DCE-Pool \
  --lr 0.001 \
  --num_epochs 100 \
  --lr_type cos \
  --train_batch_size 32 \
  --model DCE-Net-Pool \
  --snapshot_iter 10 \
  --num_workers 32 \

Evaluation of trained models

To evaluated the STAR-DCE model you trained,

cd STAR-DCE
  python test_dce.py \
  --lowlight_images_path  "dir-to-your-test-set" \
  --parallel True \
  --snapshots_folder snapshots_test/STAR-DCE \
  --val_batch_size 1 \
  --pretrain_dir snapshots/STAR-ori/Epoch_best.pth \
  --model STAR-DCE-Ori \

To evaluated the DCE-Net model you trained,

cd STAR-DCE
  python test_dce.py \
  --lowlight_images_path  "dir-to-your-test-set" \
  --parallel True \
  --snapshots_folder snapshots_test/DCE \
  --val_batch_size 1 \
  --pretrain_dir snapshots/DCE/Epoch_best.pth \
  --model DCE-Net \

Citation

If this code helps your research, please cite our paper :)

@inproceedings{zhang2021star,
  title={STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement},
  author={Zhang, Zhaoyang and Jiang, Yitong and Jiang, Jun and Wang, Xiaogang and Luo, Ping and Gu, Jinwei},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4106--4115},
  year={2021}
}
VQMIVC - Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion

VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion (Interspeech

Disong Wang 262 Dec 31, 2022
This repository contains the re-implementation of our paper deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling

deSpeckNet-TF-GEE This repository contains the re-implementation of our paper deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling publi

Adugna Mullissa 16 Sep 07, 2022
Remote sensing change detection using PaddlePaddle

Change Detection Laboratory Developing and benchmarking deep learning-based remo

Lin Manhui 15 Sep 23, 2022
基于pytorch构建cyclegan示例

cyclegan-demo 基于Pytorch构建CycleGAN示例 如何运行 准备数据集 将数据集整理成4个文件,分别命名为 trainA, trainB:训练集,A、B代表两类图片 testA, testB:测试集,A、B代表两类图片 例如 D:\CODE\CYCLEGAN-DEMO\DATA

Koorye 3 Oct 18, 2022
This repo contains the implementation of YOLOv2 in Keras with Tensorflow backend.

Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).

Huynh Ngoc Anh 1.7k Dec 24, 2022
PyTorch implementation of ECCV 2020 paper "Foley Music: Learning to Generate Music from Videos "

Foley Music: Learning to Generate Music from Videos This repo holds the code for the framework presented on ECCV 2020. Foley Music: Learning to Genera

Chuang Gan 30 Nov 03, 2022
code for `Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation`

Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation (CVPR 2021) Introduction PBR is a conceptually simple yet effective

H.Chen 143 Jan 05, 2023
Deep Q-Learning Network in pytorch (not actively maintained)

pytoch-dqn This project is pytorch implementation of Human-level control through deep reinforcement learning and I also plan to implement the followin

Hung-Tu Chen 342 Jan 01, 2023
An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models.

DeepNER An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models. This repository contains complex Deep

Derrick 9 May 30, 2022
Fast and Context-Aware Framework for Space-Time Video Super-Resolution (VCIP 2021)

Fast and Context-Aware Framework for Space-Time Video Super-Resolution Preparation Dependencies PyTorch 1.2.0 CUDA 10.0 DCNv2 cd model/DCNv2 bash make

Xueheng Zhang 1 Mar 29, 2022
[NeurIPS'20] Self-supervised Co-Training for Video Representation Learning. Tengda Han, Weidi Xie, Andrew Zisserman.

CoCLR: Self-supervised Co-Training for Video Representation Learning This repository contains the implementation of: InfoNCE (MoCo on videos) UberNCE

Tengda Han 271 Jan 02, 2023
Low-code/No-code approach for deep learning inference on devices

EzEdgeAI A concept project that uses a low-code/no-code approach to implement deep learning inference on devices. It provides a componentized framewor

On-Device AI Co., Ltd. 7 Apr 05, 2022
Code release for Convolutional Two-Stream Network Fusion for Video Action Recognition

Convolutional Two-Stream Network Fusion for Video Action Recognition

Christoph Feichtenhofer 676 Dec 31, 2022
Code accompanying the paper Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs (Chen et al., CVPR 2020, Oral).

Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs This repository contains PyTorch implementation of our pa

Shizhe Chen 178 Dec 29, 2022
PyTorch implementation for Graph Contrastive Learning with Augmentations

Graph Contrastive Learning with Augmentations PyTorch implementation for Graph Contrastive Learning with Augmentations [poster] [appendix] Yuning You*

Shen Lab at Texas A&M University 382 Dec 15, 2022
NER for Indian languages

CL-NERIL: A Cross-Lingual Model for NER in Indian Languages Code for the paper - https://arxiv.org/abs/2111.11815 Setup Setup a virtual environment Th

Akshara P 0 Nov 24, 2021
RoIAlign & crop_and_resize for PyTorch

RoIAlign for PyTorch This is a PyTorch version of RoIAlign. This implementation is based on crop_and_resize and supports both forward and backward on

Long Chen 530 Jan 07, 2023
Pytorch implementation of “Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement”

Graph-to-Graph Transformers Self-attention models, such as Transformer, have been hugely successful in a wide range of natural language processing (NL

Idiap Research Institute 40 Aug 14, 2022
Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices

Face-Mesh Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning

Farnam Javadi 9 Dec 21, 2022
《Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching》(CVPR 2020)

This contains the codes for cross-view geo-localization method described in: Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching, CVPR2020.

41 Oct 27, 2022