Metadata-Version: 2.1 Name: mmdet Version: 2.25.0 Summary: OpenMMLab Detection Toolbox and Benchmark Home-page: https://github.com/open-mmlab/mmdetection Author: MMDetection Contributors Author-email: openmmlab@gmail.com License: Apache License 2.0 Keywords: computer vision,object detection Classifier: Development Status :: 5 - Production/Stable Classifier: License :: OSI Approved :: Apache Software License Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.6 Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 Description-Content-Type: text/markdown Provides-Extra: all Provides-Extra: tests Provides-Extra: build Provides-Extra: optional License-File: LICENSE **_Weclome the discussions and contributions._** ## Introduction **BoxInstSeg** is a toolbox that aims to provide state-of-the-art box-supervised instance segmentation algorithms. It is built on top of [mmdetection](https://github.com/open-mmlab/mmdetection). The main branch works with Pytorch 1.6+ or higher (we recommend Pytorch **1.9.0**) #### Major features - **Support of instance segmentation with only box annotations** We implement multiple box-supervised instance segmentation methods in this toolbox,(*e.g.* BoxInst, DiscoBox). This toolbox can achieve the similar performance as the original paper. - **MMdetection feature inheritance** This toolbox doesn't change the structure and logic of mmdetection. It inherits all features from MMdetection. ## https://user-images.githubusercontent.com/32033843/207869884-5254b2ba-5e3d-44fb-bbd6-b26d07c9b404.mp4 ## Update - **The codes of Box2mask are available.** ## Model Zoo Supported methods - [x] [BoxInst (CVPR2021)](https://arxiv.org/abs/2012.02310) - [x] [DiscoBox (ICCV2021)](https://arxiv.org/abs/2105.06464v2) - [x] [BoxLevelset (ECCV2022)](https://arxiv.org/abs/2207.09055) - [x] [Box2Mask (arXiv2022)](https://arxiv.org/pdf/2212.01579.pdf) ### COCO (val) | method | Backbone | GPUs| Models | sched. |config | AP (this rep) | AP(original rep/paper) | |:---------------:|----------|-----------|-----------------------------------------------------------------------------------------------|:-------:|:-------:|:-------------:|:----------------------:| | BoxInst | R-50 | 8 | [model](https://drive.google.com/file/d/1dVOmUGsvnORcUGpFRXTxknepvsUYiV34/view?usp=sharing) | 1x |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/boxinst/boxinst_r50_fpn_1x_coco.py) | 30.7 | 30.7 | | BoxInst | R-50 | 8 | [model](https://drive.google.com/drive/folders/1dbBM6EMA_8lFnrMzHCAV4X7ecxYzjl4w?usp=sharing) | 3x |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/boxinst/boxinst_r50_fpn_3x_coco.py) | 32.1 | 31.8 | | BoxInst | R-101 | 8 | [model](https://drive.google.com/drive/folders/1RCFqb15bVlNaI7AxKerP6hRmJ8AnexKN?usp=sharing) | 1x |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/boxinst/boxinst_r101_fpn_1x_coco.py) | 32.0 | 32.2 | | BoxInst | R-101 | 8 | [model](https://drive.google.com/file/d/1tlXLL5Ba9_o5V7zn18KZPCIyEJdpZFV1/view?usp=sharing) | 3x |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/boxinst/boxinst_r101_fpn_3x_coco.py) | 33.1 | 33.0 | | DiscoBox | R-50 | 8 | [model](https://drive.google.com/file/d/1ifhmjXbFCBsn6wLBeOM6BWOm11LDmecr/view?usp=sharing) | 3x |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/discobox/discobox_solov2_coco_r50_fpn_3x.py) | 32.2 | 31.4(wo ms) | | DiscoBox | R-101 | 8 | [model](https://drive.google.com/file/d/12yNKThkQK3yV8B5YHkkgyMOa0e5-uUpJ/view?usp=sharing) | 3x |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/discobox/discobox_solov2_coco_r101_fpn_3x.py) | 33.4 | -- | | Box2Mask-T | R-50 | 8 | [model](https://drive.google.com/file/d/1KFJabXdGodgcO-GerNpJkgyZX9whnNYe/view?usp=sharing) | 50e | [config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/box2mask/box2mask_r50_lsj_8x2_50e_coco.py) | 35.9 | 36.1 | | Box2Mask-T | R-101 | 8 | [model](https://drive.google.com/file/d/120L2hn7MZChc1ZaCcH37bjYe6d3sN2Op/view?usp=sharing) | 50e | [config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/box2mask/box2mask_r101_lsj_8x2_50e_coco.py) | 38.2 | 37.9 | | Box2Mask-T | Swin-L | 8 | [model](https://drive.google.com/file/d/1EKa4cna_A0ec-HFL8jli_FCqXSZ3bWhV/view?usp=sharing) | 50e | [config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/box2mask/box2mask_swin-l-p4-w12-384-lsj_8x1_50e_coco.py) | 41.9/42.5 | 41.3/42.4 | - The above models are trained with `ms` to make a performance comparison. - For Swin-L model, the result of `a/b` format is on `val/test-dev` set. A100 GPUs are used for the default config. ### Pascal VOC | method | Backbone | GPUs | Models | sched. |config | AP | AP_50| AP_75| |:---------------:|----------|-----------|-----------|:-------:|:-------:|:-------:|:-------:|:-------:| | BoxInst | R-50 |4 | [model](https://drive.google.com/drive/folders/18ZK3uqAtcRx9r-Ci45Bg358LA4eFGBsY?usp=sharing) | 3x |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/boxinst/boxinst_r50_fpn_3x_voc.py) | 32.0 | 60.2 | 30.2 | | BoxInst | R-101 | 4| [model](https://drive.google.com/drive/folders/1QA9bSUnwtJ-pyuj_4pqHA9X64g3PbisT?usp=sharing) | 3x |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/boxinst/boxinst_r101_fpn_3x_voc.py) | 34.2 | 62.4| 33.2 | | DiscoBox | R-50 | 4|[model](https://drive.google.com/file/d/1uNO_YVhGN5Kwbc8fjjtFCxULnRclZ_IW/view?usp=sharing) | 3x |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/discobox/discobox_solov2_voc_r50_fpn_3x.py) | 32.9 | 61.0 | 31.5 | | DiscoBox | R-101 | 4| [model](https://drive.google.com/drive/folders/1JChdGB0mBjC4ypNuNxbCuIdjj4YY7GBv?usp=sharing) | 3x |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/discobox/discobox_solov2_voc_r101_fpn_3x.py) | 34.6 |63.0 | 33.0 | | Box2Mask-T | R-50 | 4 |model | 50e |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/box2mask/box2mask_r50_lsj_8x2_50e_voc.py) | 38.0 | 65.9 | 38.2 | | Box2Mask-T | R-101 | 4 |model | 50e |[config](https://github.com/LiWentomng/BoxInstSeg/blob/main/configs/box2mask/box2mask_r101_lsj_8x2_50e_voc.py) | 39.6 | 66.6 | 40.9 | - **Pascal VOC** is the extension of the training set of VOC 2012 with SBD. The link of whole dataset with coco json format is [here](https://drive.google.com/file/d/16Mz13NSZBbhwPuRxiwi7ZA2Qvt9DaKtN/view?usp=sharing)(GoogleDrive) ## Installation and Getting Started This is built on the MMdetection (V2.25.0). Please refer to [Installation](https://github.com/LiWentomng/BoxInstSeg/blob/main/docs/install.md) and [Getting Started](https://github.com/LiWentomng/BoxInstSeg/blob/main/docs/get_started.md) for the details of installation and basic usage. We also recommend the user to refer the office [introduction](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md/#Installation) of MMdetection. ## License This project is released under the [Apache 2.0 license](LICENSE). ## Acknowledgement This project is built based on [MMdetection](https://github.com/open-mmlab/mmdetection) and part of module is borrowed from the original rep of [Adelaidet](https://github.com/aim-uofa/AdelaiDet) and [DiscoBox](https://github.com/NVlabs/DiscoBox). ## More - This [repo](https://github.com/LiWentomng/Box-supervised-instance-segmentation) will update the **survey** of _box-supervised instance segmentation_, we highly welcome the user to develop more algorithms in this toolbox. - **_If this rep is helpful for your work, please give me a star._**