This is the official repository for our two papers:
In the two papers, we propose a new task named One-shot Human Parsing (OSHP). OSHP requires parsing humans in a query image into an open set of reference classes defined by any single reference example (i.e., a support image) during testing, no matter whether they have been annotated during training (base classes) or not (novel classes). This new task mainly aims to accommodate human parsing into a wider range of applications that seek to parse flexible fashion/clothing classes that are not pre-defined in previous large-scale datasets.
Progressive One-shot Human Parsing (AAAI 2021) introduces the new tasks and applies a progressive training scheme and is separated into three stages.
End-to-end One-shot Human Parsing (TPAMI 2023) describes a one-stage end-to-end training method with high performance and FPS. It also provides a complete task description and more baseline methods.
You can find the well-trained k-way models together with the performance in the following table.
| EOPNet | ATR-OS, Kway F1 | ATR-OS, Kway Fold F2 | LIP-OS, Kway F1 | LIP-OS, Kway F2 | CIHP-OS, Kway F1 | CIHP-OS Kway F2 |
|---|---|---|---|---|---|---|
| Novel mIoU | 31.1 | 34.6 | 25.7 | 30.4 | 20.5 | 25.1 |
| Human mIoU | 61.9 | 63.3 | 43.0 | 45.7 | 49.1 | 45.5 |
| Model | Model | Coming Soon | Model | Model | Model | Model |
First, please download ATR, LIP and CIHP dataset from source. Then, use the following commands to link the data into our project folder. Please also remember to download the atr flipped labels and cihp flipped labels.
# ATR dataset
$ ln -s YOUR_ATR_PATH/JPEGImages/* YOUR_PROJECT_ROOT/ATR_OS/trainval_images
$ ln -s YOUR_ATR_PATH/SegmentationClassAug/* YOUR_PROJECT_ROOT/ATR_OS/trainval_classes
$ ln -s YOUR_ATR_PATH/SegmentationClassAug_rev/* YOUR_PROJECT_ROOT/ATR_OS/Category_rev_ids
# LIP dataset
$ ln -s YOUR_LIP_PATH/TrainVal_images/TrainVal_images/train_images/* YOUR_PROJECT_ROOT/LIP_OS/trainval_images
$ ln -s YOUR_LIP_PATH/TrainVal_images/TrainVal_images/val_images/* YOUR_PROJECT_ROOT/LIP_OS/trainval_images
$ ln -s YOUR_LIP_PATH/TrainVal_parsing_annotations/TrainVal_parsing_annotations/train_segmentations/* YOUR_PROJECT_ROOT/LIP_OS/trainval_classes
$ ln -s YOUR_LIP_PATH/TrainVal_parsing_annotations/TrainVal_parsing_annotations/val_segmentations/* YOUR_PROJECT_ROOT/LIP_OS/trainval_classes
$ ln -s YOUR_LIP_PATH/Train_parsing_reversed_labels/TrainVal_parsing_annotations/* YOUR_PROJECT_ROOT/LIP_OS/Category_rev_ids
$ ln -s YOUR_LIP_PATH/val_segmentations_reversed/* YOUR_PROJECT_ROOT/LIP_OS/Category_rev_ids
# CIHP dataset
$ ln -s YOUR_CIHP_PATH/Training/Images/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_images
$ ln -s YOUR_CIHP_PATH/Validation/Images/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_images
$ ln -s YOUR_CIHP_PATH/Training/Category_ids/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_classes
$ ln -s YOUR_CIHP_PATH/Validation/Category_ids/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_classes
$ ln -s YOUR_CIHP_PATH/Category_rev_ids/* YOUR_PROJECT_ROOT/CIHP_OS/Category_rev_ids
Please also download our generated support .pkl files from source, which contains each class's image IDs. You can also generate support files on your own by controlling dtrain_dtest_split in oshp_loader.py, however, the training and validation list might be different from our paper.
Finally, your data folder should look like this:
${PROJECT ROOT}
|-- data
| |--datasets
| |-- ATR_OS
| | |-- list
| | | |-- meta_train_id.txt
| | | `-- meta_test_id.txt
| | |-- support
| | | |-- meta_train_atr_supports.pkl
| | | `-- meta_test_atr_supports.pkl
| | |-- trainval_images
| | | |-- 997-1.jpg
| | | |-- 997-2.jpg
| | | `-- ...
| | |-- trainval_classes
| | | |-- 997-1.png
| | | |-- 997-2.png
| | | `-- ...
| | `-- Category_rev_ids
| | |-- 997-1.png
| | |-- 997-2.png
| | `-- ...
| |-- LIP_OS
| | |-- list
| | | |-- meta_train_id.txt
| | | |-- meta_test_id.txt
| | |-- support
| | | |-- meta_train_lip_supports.pkl
| | | `-- meta_test_lip_supports.pkl
| | |-- trainval_images
| | | |-- ...
| | |-- trainval_classes
| | | |-- ...
| | `-- Category_rev_ids
| | |-- ...
| `-- CIHP_OS
| |-- list
| | |-- meta_train_id.txt
| | |-- meta_test_id.txt
| |-- support
| | |-- meta_train_cihp_supports.pkl
| | `-- meta_test_cihp_supports.pkl
| |-- trainval_images
| | |-- ...
| |-- trainval_classes
| | |-- ...
| `-- Category_rev_ids
| |-- ...
Finally, please download the DeepLab V3+ pretrained model (pretrained on COCO dataset) from source and put it into the data folder:
${PROJECT ROOT}
|-- data
| |--pretrained_model
| |--deeplab_v3plus_v3.pth
Please make sure your current environment has Python >= 3.7.0 and pytorch >= 1.1.0. The pytorch can be downloaded from source.
Then, clone the repository and install the dependencies from the following commands:
git clone https://github.com/Charleshhy/One-shot-Human-Parsing.git
cd One-shot-Human-Parsing
pip install -r requirements.txt
To train EOPNet in End-to-end One-shot Human Parsing (journal version), run:
# OSHP kway on ATR-OS fold 1
bash scripts/atr_eop_kwf1.shTo evaluate EOPNet in End-to-end One-shot Human Parsing (journal version), run:
# OSHP kway on ATR-OS fold 1
bash scripts/evaluate_atr_eop_kwf1.shIf you find our papers or this repository helpful, please consider citing our papers:
@inproceedings{he2021progressive,
title={Progressive One-shot Human Parsing},
author={He, Haoyu and Zhang, Jing and Thuraisingham, Bhavani and Tao, Dacheng},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2021}
}
@article{he2023end,
title={End-to-end One-shot Human Parsing},
author={He, Haoyu and Zhang, Jing and Zhuang, Bohan and Cai, Jianfei and Tao, Dacheng},
journal={TPAMI},
year={2023}
}
This repository is mainly developed based on Graphonomy and Grapy-ML.