# InstanceShadowDetection

**Repository Path**: gc-mirrors/InstanceShadowDetection

## Basic Information

- **Project Name**: InstanceShadowDetection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-04-20
- **Last Updated**: 2026-04-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Instance Shadow Detection (CVPR’ 20)

[Tianyu Wang](https://stevewongv.github.io)\*, [Xiaowei Hu](https://xw-hu.github.io)\*, Qiong Wang,  Pheng-Ann Heng,  and [Chi-Wing Fu](http://www.cse.cuhk.edu.hk/~cwfu/)
 (\* Joint first authors.)

[[`openaccess`](http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Instance_Shadow_Detection_CVPR_2020_paper.pdf)][[`arXiv`](https://arxiv.org/abs/1911.07034)] [[`BibTeX`](#CitingLISA)]

**News: Our new work on instance shadow detection was accepted in CVPR 2021 as Oral presentation, check [here](https://github.com/stevewongv/SSIS)!**




![-c](demo/demo.jpeg)

Instance shadow detection aims to find shadow instances paired with object instances. We present a dataset, a deep framework, and an evaluation metric to approach this new task. This repo is implemented on [Detectron2](https://github.com/facebookresearch/detectron2).


## Dependences

* python>=3.6
* torch (tested on 1.3.0+cu100 and 1.12.0+cu113)
* torchvision (tested on 0.4.1+cu100 and 0.13.0+cu113)
* tensorboard
* cython
* jupyter
* scikit-image
* numpy
* opencv-python
* pycocotools




## Installation 

Install LISA and pysobatools

```bash
$ cd InstanceShadowDetection
$ python setup.py install
$ cd PythonAPI
$ python setup.py install
```

## Docker

```bash
$ cd InstanceShadowDetection/docker

$ docker build --network=host --tag="instanceshadow" -f ./Dockerfile .

$ docker run --gpus all -it --ipc=host --name=instanceshadow --network=host -v /YOURPATH:/data instanceshadow:latest
```

**(Nvidia-docker)[https://github.com/NVIDIA/nvidia-docker] is needed.**

## Model, dataset and our results

Please download from [Google Drive](https://drive.google.com/drive/folders/1MKxyq3R6AUeyLai9i9XWzG2C_n5f0ppP). Put the model and dataset follow the directory layout below.

    .
    ├── ...
    ├── dataset
    │   ├── SOBA                # put dataset here
    ├── InstanceShadowDetection # this repo
    │   ├── projects
    │   │   ├── LISA
    │   │   │   ├── output_light
    │   │   │   │   ├── last_checkpoint.pth
    │   │   │   │   └── ...
    │   │   │   └── ...
    │   └── ...
    └── ...


## Demo

```bash
$ cd projects/LISA/
$ python demo.py --input ./demo/web-shadow0573.jpg --output ./ --config ./config/LISA_101_FPN_3x_demo.yaml
```

## Train

```bash
$ python train_net.py --num-gpus 2 --config-file ./config/LISA_101_FPN_3x.yaml

```
## Evaluation

```bash
$ python train_net.py --num-gpus 2 --config-file ./config/LISA_101_FPN_3x.yaml --eval-only --resume
$ python SOAP.py
```

## Visualize
```bash
python visualize_json_results.py --ins_input ./output_light/inference/soba_instances_results.json --ass_input ./output_light/inference/soba_association_results.json --output ./output_light/results --dataset soba_cast_shadow_val_full
```
## <a name="CitingLISA"></a> Citation
If you use LISA, SISS, SOBA, or SOAP, please use the following BibTeX entry.

```
@InProceedings{Wang_2020_CVPR,
author = {Wang, Tianyu and Hu, Xiaowei and Wang, Qiong and Heng, Pheng-Ann and Fu, Chi-Wing},
title = {Instance Shadow Detection},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

@InProceedings{Wang_2021_CVPR,
author    = {Wang, Tianyu and Hu, Xiaowei and Fu, Chi-Wing and Heng, Pheng-Ann},
title     = {Single-Stage Instance Shadow Detection With Bidirectional Relation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month     = {June},
Year      = {2021},
pages     = {1-11}
}
```