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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/en/_build/
docs/zh_cn/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
data/
data
.vscode
.idea
.DS_Store
# custom
*.pkl
*.pkl.json
*.log.json
docs/modelzoo_statistics.md
mmdet/.mim
work_dirs/
# Pytorch
*.pth
*.py~
*.sh~
.owners.yml 0 → 100644
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assign:
strategy:
# random
daily-shift-based
scedule:
'*/1 * * * *'
assignees:
- Czm369
- hhaAndroid
- jbwang1997
- RangiLyu
- BIGWangYuDong
- chhluo
- ZwwWayne
.pre-commit-config-zh-cn.yaml 0 → 100644
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exclude: ^tests/data/
repos:
- repo: https://gitee.com/openmmlab/mirrors-flake8
rev: 5.0.4
hooks:
- id: flake8
- repo: https://gitee.com/openmmlab/mirrors-isort
rev: 5.11.5
hooks:
- id: isort
- repo: https://gitee.com/openmmlab/mirrors-yapf
rev: v0.32.0
hooks:
- id: yapf
- repo: https://gitee.com/openmmlab/mirrors-pre-commit-hooks
rev: v4.3.0
hooks:
- id: trailing-whitespace
- id: check-yaml
- id: end-of-file-fixer
- id: requirements-txt-fixer
- id: double-quote-string-fixer
- id: check-merge-conflict
- id: fix-encoding-pragma
args: ["--remove"]
- id: mixed-line-ending
args: ["--fix=lf"]
- repo: https://gitee.com/openmmlab/mirrors-mdformat
rev: 0.7.9
hooks:
- id: mdformat
args: ["--number"]
additional_dependencies:
- mdformat-openmmlab
- mdformat_frontmatter
- linkify-it-py
- repo: https://gitee.com/openmmlab/mirrors-codespell
rev: v2.2.1
hooks:
- id: codespell
- repo: https://gitee.com/openmmlab/mirrors-docformatter
rev: v1.3.1
hooks:
- id: docformatter
args: ["--in-place", "--wrap-descriptions", "79"]
- repo: https://gitee.com/openmmlab/mirrors-pyupgrade
rev: v3.0.0
hooks:
- id: pyupgrade
args: ["--py36-plus"]
- repo: https://gitee.com/open-mmlab/pre-commit-hooks
rev: v0.2.0
hooks:
- id: check-algo-readme
- id: check-copyright
args: ["mmdet"]
# - repo: https://gitee.com/openmmlab/mirrors-mypy
# rev: v0.812
# hooks:
# - id: mypy
# exclude: "docs"
.pre-commit-config.yaml 0 → 100644
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repos:
- repo: https://github.com/PyCQA/flake8
rev: 5.0.4
hooks:
- id: flake8
- repo: https://github.com/PyCQA/isort
rev: 5.11.5
hooks:
- id: isort
- repo: https://github.com/pre-commit/mirrors-yapf
rev: v0.32.0
hooks:
- id: yapf
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.3.0
hooks:
- id: trailing-whitespace
- id: check-yaml
- id: end-of-file-fixer
- id: requirements-txt-fixer
- id: double-quote-string-fixer
- id: check-merge-conflict
- id: fix-encoding-pragma
args: ["--remove"]
- id: mixed-line-ending
args: ["--fix=lf"]
- repo: https://github.com/codespell-project/codespell
rev: v2.2.1
hooks:
- id: codespell
- repo: https://github.com/executablebooks/mdformat
rev: 0.7.9
hooks:
- id: mdformat
args: ["--number"]
additional_dependencies:
- mdformat-openmmlab
- mdformat_frontmatter
- linkify-it-py
- repo: https://github.com/myint/docformatter
rev: v1.3.1
hooks:
- id: docformatter
args: ["--in-place", "--wrap-descriptions", "79"]
- repo: https://github.com/open-mmlab/pre-commit-hooks
rev: v0.2.0 # Use the ref you want to point at
hooks:
- id: check-algo-readme
- id: check-copyright
args: ["mmdet"] # replace the dir_to_check with your expected directory to check
.readthedocs.yml 0 → 100644
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version: 2
build:
os: ubuntu-22.04
tools:
python: "3.8"
formats:
- epub
python:
install:
- requirements: requirements/docs.txt
- requirements: requirements/readthedocs.txt
CITATION.cff 0 → 100644
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cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- name: "MMDetection Contributors"
title: "OpenMMLab Detection Toolbox and Benchmark"
date-released: 2018-08-22
url: "https://github.com/open-mmlab/mmdetection"
license: Apache-2.0
LICENSE 0 → 100644
View file @ 4a950602
Copyright 2018-2023 OpenMMLab. All rights reserved.
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MANIFEST.in 0 → 100644
View file @ 4a950602
include requirements/*.txt
include mmdet/VERSION
include mmdet/.mim/model-index.yml
include mmdet/.mim/dataset-index.yml
include mmdet/.mim/demo/*/*
recursive-include mmdet/.mim/configs *.py *.yml
recursive-include mmdet/.mim/tools *.sh *.py
README _ori.md 0 → 100644
View file @ 4a950602
<div align="center">
<img src="resources/mmdet-logo.png" width="600"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div>&nbsp;</div>
[![PyPI](https://img.shields.io/pypi/v/mmdet)](https://pypi.org/project/mmdet)
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection.readthedocs.io/en/latest/)
[![badge](https://github.com/open-mmlab/mmdetection/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmdetection/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection)
[![license](https://img.shields.io/github/license/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/blob/main/LICENSE)
[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_demo.svg)](https://openxlab.org.cn/apps?search=mmdet)
[📘Documentation](https://mmdetection.readthedocs.io/en/latest/) |
[🛠️Installation](https://mmdetection.readthedocs.io/en/latest/get_started.html) |
[👀Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html) |
[🆕Update News](https://mmdetection.readthedocs.io/en/latest/notes/changelog.html) |
[🚀Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose)
</div>
<div align="center">
English | [简体中文](README_zh-CN.md)
</div>
<div align="center">
<a href="https://openmmlab.medium.com/" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://discord.com/channels/1037617289144569886/1046608014234370059" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
</div>
<div align="center">
<img src="https://github.com/open-mmlab/mmdetection/assets/17425982/6c29886f-ae7a-4a55-8be4-352ee85b7d3e"/>
</div>
## Introduction
MMDetection is an open source object detection toolbox based on PyTorch. It is
a part of the [OpenMMLab](https://openmmlab.com/) project.
The main branch works with **PyTorch 1.8+**.
<img src="https://user-images.githubusercontent.com/12907710/187674113-2074d658-f2fb-42d1-ac15-9c4a695e64d7.png"/>
<details open>
<summary>Major features</summary>
- **Modular Design**
We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.
- **Support of multiple tasks out of box**
The toolbox directly supports multiple detection tasks such as **object detection**, **instance segmentation**, **panoptic segmentation**, and **semi-supervised object detection**.
- **High efficiency**
All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet).
- **State of the art**
The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward.
The newly released [RTMDet](configs/rtmdet) also obtains new state-of-the-art results on real-time instance segmentation and rotated object detection tasks and the best parameter-accuracy trade-off on object detection.
</details>
Apart from MMDetection, we also released [MMEngine](https://github.com/open-mmlab/mmengine) for model training and [MMCV](https://github.com/open-mmlab/mmcv) for computer vision research, which are heavily depended on by this toolbox.
## What's New
💎 **We have released the pre-trained weights for MM-Grounding-DINO Swin-B and Swin-L, welcome to try and give feedback.**
### Highlight
**v3.3.0** was released in 5/1/2024:
**[MM-Grounding-DINO: An Open and Comprehensive Pipeline for Unified Object Grounding and Detection](https://arxiv.org/abs/2401.02361)**
Grounding DINO is a grounding pre-training model that unifies 2d open vocabulary object detection and phrase grounding, with wide applications. However, its training part has not been open sourced. Therefore, we propose MM-Grounding-DINO, which not only serves as an open source replication version of Grounding DINO, but also achieves significant performance improvement based on reconstructed data types, exploring different dataset combinations and initialization strategies. Moreover, we conduct evaluations from multiple dimensions, including OOD, REC, Phrase Grounding, OVD, and Fine-tune, to fully excavate the advantages and disadvantages of Grounding pre-training, hoping to provide inspiration for future work.
code: [mm_grounding_dino/README.md](configs/mm_grounding_dino/README.md)
<div align=center>
<img src="https://github.com/open-mmlab/mmdetection/assets/17425982/fb14d1ee-5469-44d2-b865-aac9850c429c"/>
</div>
We are excited to announce our latest work on real-time object recognition tasks, **RTMDet**, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the [technical report](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](configs/rtmdet).
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/real-time-instance-segmentation-on-mscoco)](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-dota-1)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-hrsc2016)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
| Task | Dataset | AP | FPS(TRT FP16 BS1 3090) |
| ------------------------ | ------- | ------------------------------------ | ---------------------- |
| Object Detection | COCO | 52.8 | 322 |
| Instance Segmentation | COCO | 44.6 | 188 |
| Rotated Object Detection | DOTA | 78.9(single-scale)/81.3(multi-scale) | 121 |
<div align=center>
<img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
</div>
## Installation
Please refer to [Installation](https://mmdetection.readthedocs.io/en/latest/get_started.html) for installation instructions.
## Getting Started
Please see [Overview](https://mmdetection.readthedocs.io/en/latest/get_started.html) for the general introduction of MMDetection.
For detailed user guides and advanced guides, please refer to our [documentation](https://mmdetection.readthedocs.io/en/latest/):
- User Guides
<details>
- [Train & Test](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#train-test)
- [Learn about Configs](https://mmdetection.readthedocs.io/en/latest/user_guides/config.html)
- [Inference with existing models](https://mmdetection.readthedocs.io/en/latest/user_guides/inference.html)
- [Dataset Prepare](https://mmdetection.readthedocs.io/en/latest/user_guides/dataset_prepare.html)
- [Test existing models on standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/test.html)
- [Train predefined models on standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/train.html)
- [Train with customized datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/train.html#train-with-customized-datasets)
- [Train with customized models and standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/new_model.html)
- [Finetuning Models](https://mmdetection.readthedocs.io/en/latest/user_guides/finetune.html)
- [Test Results Submission](https://mmdetection.readthedocs.io/en/latest/user_guides/test_results_submission.html)
- [Weight initialization](https://mmdetection.readthedocs.io/en/latest/user_guides/init_cfg.html)
- [Use a single stage detector as RPN](https://mmdetection.readthedocs.io/en/latest/user_guides/single_stage_as_rpn.html)
- [Semi-supervised Object Detection](https://mmdetection.readthedocs.io/en/latest/user_guides/semi_det.html)
- [Useful Tools](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#useful-tools)
</details>
- Advanced Guides
<details>
- [Basic Concepts](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#basic-concepts)
- [Component Customization](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#component-customization)
- [How to](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#how-to)
</details>
We also provide object detection colab tutorial [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_Tutorial.ipynb) and instance segmentation colab tutorial [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_InstanceSeg_Tutorial.ipynb).
To migrate from MMDetection 2.x, please refer to [migration](https://mmdetection.readthedocs.io/en/latest/migration.html).
## Overview of Benchmark and Model Zoo
Results and models are available in the [model zoo](docs/en/model_zoo.md).
<div align="center">
<b>Architectures</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Object Detection</b>
</td>
<td>
<b>Instance Segmentation</b>
</td>
<td>
<b>Panoptic Segmentation</b>
</td>
<td>
<b>Other</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<li><a href="configs/fast_rcnn">Fast R-CNN (ICCV'2015)</a></li>
<li><a href="configs/faster_rcnn">Faster R-CNN (NeurIPS'2015)</a></li>
<li><a href="configs/rpn">RPN (NeurIPS'2015)</a></li>
<li><a href="configs/ssd">SSD (ECCV'2016)</a></li>
<li><a href="configs/retinanet">RetinaNet (ICCV'2017)</a></li>
<li><a href="configs/cascade_rcnn">Cascade R-CNN (CVPR'2018)</a></li>
<li><a href="configs/yolo">YOLOv3 (ArXiv'2018)</a></li>
<li><a href="configs/cornernet">CornerNet (ECCV'2018)</a></li>
<li><a href="configs/grid_rcnn">Grid R-CNN (CVPR'2019)</a></li>
<li><a href="configs/guided_anchoring">Guided Anchoring (CVPR'2019)</a></li>
<li><a href="configs/fsaf">FSAF (CVPR'2019)</a></li>
<li><a href="configs/centernet">CenterNet (CVPR'2019)</a></li>
<li><a href="configs/libra_rcnn">Libra R-CNN (CVPR'2019)</a></li>
<li><a href="configs/tridentnet">TridentNet (ICCV'2019)</a></li>
<li><a href="configs/fcos">FCOS (ICCV'2019)</a></li>
<li><a href="configs/reppoints">RepPoints (ICCV'2019)</a></li>
<li><a href="configs/free_anchor">FreeAnchor (NeurIPS'2019)</a></li>
<li><a href="configs/cascade_rpn">CascadeRPN (NeurIPS'2019)</a></li>
<li><a href="configs/foveabox">Foveabox (TIP'2020)</a></li>
<li><a href="configs/double_heads">Double-Head R-CNN (CVPR'2020)</a></li>
<li><a href="configs/atss">ATSS (CVPR'2020)</a></li>
<li><a href="configs/nas_fcos">NAS-FCOS (CVPR'2020)</a></li>
<li><a href="configs/centripetalnet">CentripetalNet (CVPR'2020)</a></li>
<li><a href="configs/autoassign">AutoAssign (ArXiv'2020)</a></li>
<li><a href="configs/sabl">Side-Aware Boundary Localization (ECCV'2020)</a></li>
<li><a href="configs/dynamic_rcnn">Dynamic R-CNN (ECCV'2020)</a></li>
<li><a href="configs/detr">DETR (ECCV'2020)</a></li>
<li><a href="configs/paa">PAA (ECCV'2020)</a></li>
<li><a href="configs/vfnet">VarifocalNet (CVPR'2021)</a></li>
<li><a href="configs/sparse_rcnn">Sparse R-CNN (CVPR'2021)</a></li>
<li><a href="configs/yolof">YOLOF (CVPR'2021)</a></li>
<li><a href="configs/yolox">YOLOX (CVPR'2021)</a></li>
<li><a href="configs/deformable_detr">Deformable DETR (ICLR'2021)</a></li>
<li><a href="configs/tood">TOOD (ICCV'2021)</a></li>
<li><a href="configs/ddod">DDOD (ACM MM'2021)</a></li>
<li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
<li><a href="configs/conditional_detr">Conditional DETR (ICCV'2021)</a></li>
<li><a href="configs/dab_detr">DAB-DETR (ICLR'2022)</a></li>
<li><a href="configs/dino">DINO (ICLR'2023)</a></li>
<li><a href="configs/glip">GLIP (CVPR'2022)</a></li>
<li><a href="configs/ddq">DDQ (CVPR'2023)</a></li>
<li><a href="projects/DiffusionDet">DiffusionDet (ArXiv'2023)</a></li>
<li><a href="projects/EfficientDet">EfficientDet (CVPR'2020)</a></li>
<li><a href="projects/ViTDet">ViTDet (ECCV'2022)</a></li>
<li><a href="projects/Detic">Detic (ECCV'2022)</a></li>
<li><a href="projects/CO-DETR">CO-DETR (ICCV'2023)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/mask_rcnn">Mask R-CNN (ICCV'2017)</a></li>
<li><a href="configs/cascade_rcnn">Cascade Mask R-CNN (CVPR'2018)</a></li>
<li><a href="configs/ms_rcnn">Mask Scoring R-CNN (CVPR'2019)</a></li>
<li><a href="configs/htc">Hybrid Task Cascade (CVPR'2019)</a></li>
<li><a href="configs/yolact">YOLACT (ICCV'2019)</a></li>
<li><a href="configs/instaboost">InstaBoost (ICCV'2019)</a></li>
<li><a href="configs/solo">SOLO (ECCV'2020)</a></li>
<li><a href="configs/point_rend">PointRend (CVPR'2020)</a></li>
<li><a href="configs/detectors">DetectoRS (ArXiv'2020)</a></li>
<li><a href="configs/solov2">SOLOv2 (NeurIPS'2020)</a></li>
<li><a href="configs/scnet">SCNet (AAAI'2021)</a></li>
<li><a href="configs/queryinst">QueryInst (ICCV'2021)</a></li>
<li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
<li><a href="configs/condinst">CondInst (ECCV'2020)</a></li>
<li><a href="projects/SparseInst">SparseInst (CVPR'2022)</a></li>
<li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
<li><a href="configs/boxinst">BoxInst (CVPR'2021)</a></li>
<li><a href="projects/ConvNeXt-V2">ConvNeXt-V2 (Arxiv'2023)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/panoptic_fpn">Panoptic FPN (CVPR'2019)</a></li>
<li><a href="configs/maskformer">MaskFormer (NeurIPS'2021)</a></li>
<li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
<li><a href="configs/XDecoder">XDecoder (CVPR'2023)</a></li>
</ul>
</td>
<td>
</ul>
<li><b>Contrastive Learning</b></li>
<ul>
<ul>
<li><a href="configs/selfsup_pretrain">SwAV (NeurIPS'2020)</a></li>
<li><a href="configs/selfsup_pretrain">MoCo (CVPR'2020)</a></li>
<li><a href="configs/selfsup_pretrain">MoCov2 (ArXiv'2020)</a></li>
</ul>
</ul>
</ul>
<li><b>Distillation</b></li>
<ul>
<ul>
<li><a href="configs/ld">Localization Distillation (CVPR'2022)</a></li>
<li><a href="configs/lad">Label Assignment Distillation (WACV'2022)</a></li>
</ul>
</ul>
<li><b>Semi-Supervised Object Detection</b></li>
<ul>
<ul>
<li><a href="configs/soft_teacher">Soft Teacher (ICCV'2021)</a></li>
</ul>
</ul>
</ul>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
<div align="center">
<b>Components</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Backbones</b>
</td>
<td>
<b>Necks</b>
</td>
<td>
<b>Loss</b>
</td>
<td>
<b>Common</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<li>VGG (ICLR'2015)</li>
<li>ResNet (CVPR'2016)</li>
<li>ResNeXt (CVPR'2017)</li>
<li>MobileNetV2 (CVPR'2018)</li>
<li><a href="configs/hrnet">HRNet (CVPR'2019)</a></li>
<li><a href="configs/empirical_attention">Generalized Attention (ICCV'2019)</a></li>
<li><a href="configs/gcnet">GCNet (ICCVW'2019)</a></li>
<li><a href="configs/res2net">Res2Net (TPAMI'2020)</a></li>
<li><a href="configs/regnet">RegNet (CVPR'2020)</a></li>
<li><a href="configs/resnest">ResNeSt (ArXiv'2020)</a></li>
<li><a href="configs/pvt">PVT (ICCV'2021)</a></li>
<li><a href="configs/swin">Swin (CVPR'2021)</a></li>
<li><a href="configs/pvt">PVTv2 (ArXiv'2021)</a></li>
<li><a href="configs/resnet_strikes_back">ResNet strikes back (ArXiv'2021)</a></li>
<li><a href="configs/efficientnet">EfficientNet (ArXiv'2021)</a></li>
<li><a href="configs/convnext">ConvNeXt (CVPR'2022)</a></li>
<li><a href="projects/ConvNeXt-V2">ConvNeXtv2 (ArXiv'2023)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/pafpn">PAFPN (CVPR'2018)</a></li>
<li><a href="configs/nas_fpn">NAS-FPN (CVPR'2019)</a></li>
<li><a href="configs/carafe">CARAFE (ICCV'2019)</a></li>
<li><a href="configs/fpg">FPG (ArXiv'2020)</a></li>
<li><a href="configs/groie">GRoIE (ICPR'2020)</a></li>
<li><a href="configs/dyhead">DyHead (CVPR'2021)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/ghm">GHM (AAAI'2019)</a></li>
<li><a href="configs/gfl">Generalized Focal Loss (NeurIPS'2020)</a></li>
<li><a href="configs/seesaw_loss">Seasaw Loss (CVPR'2021)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/faster_rcnn/faster-rcnn_r50_fpn_ohem_1x_coco.py">OHEM (CVPR'2016)</a></li>
<li><a href="configs/gn">Group Normalization (ECCV'2018)</a></li>
<li><a href="configs/dcn">DCN (ICCV'2017)</a></li>
<li><a href="configs/dcnv2">DCNv2 (CVPR'2019)</a></li>
<li><a href="configs/gn+ws">Weight Standardization (ArXiv'2019)</a></li>
<li><a href="configs/pisa">Prime Sample Attention (CVPR'2020)</a></li>
<li><a href="configs/strong_baselines">Strong Baselines (CVPR'2021)</a></li>
<li><a href="configs/resnet_strikes_back">Resnet strikes back (ArXiv'2021)</a></li>
</ul>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
Some other methods are also supported in [projects using MMDetection](./docs/en/notes/projects.md).
## FAQ
Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions.
## Contributing
We appreciate all contributions to improve MMDetection. Ongoing projects can be found in out [GitHub Projects](https://github.com/open-mmlab/mmdetection/projects). Welcome community users to participate in these projects. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
## Acknowledgement
MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors.
## Citation
If you use this toolbox or benchmark in your research, please cite this project.
```
@article{mmdetection,
title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
journal= {arXiv preprint arXiv:1906.07155},
year={2019}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Projects in OpenMMLab
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.
- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.
README.md
View file @ 4a950602
# co-detr_mmcv # co-detr_mmcv
## 论文
`DETRs with Collaborative Hybrid Assignments Training`<br>
[论文链接](https://arxiv.org/abs/2211.12860)<br>
## 模型结构
为了缓解解码器中较少的正向查询对编码器输出的稀疏监督，我们引入了具有不同一对多标签分配范式的通用辅助头，例如ATSS和Faster R-CNN。不同的标签分配丰富了对编码器输出的监督，这迫使它具有足够的鉴别力，以支持这些头部的训练收敛。
辅助分支只在训练时使用，推理时不需要。
![model_framework.png](asserts%2Fmodel_framework.png)
## 算法原理
co-detr通过增加辅助分支优化query稀疏问题--由于使用匈牙利批量，每个真值只能匹配1个query导致的。辅助分支利用一对多的样本匹配方法，比如atss,faster-rcnn等，一个真值匹配多个预测值，提高样本数量。
![model_head.png](asserts%2Fmodel_head.png)
## 环境配置
### Docker（方法一）
Co-DETR for learn more efficient and effective DETR-based detectors from versatile label assignment manners ```
\ No newline at end of file docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/co-detr_mmcv
pip install mmdet -i https://mirrors.aliyun.com/pypi/simple/
```
### Dockerfile（方法二）
```
cd ./docker
docker build --no-cache -t mmdet:last .
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/co-detr_mmcv
pip install mmdet -i https://mirrors.aliyun.com/pypi/simple/
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装： https://developer.hpccube.com/tool/
```
DTK软件栈: dtk24.04
python: python3.10
torch: 2.1
mmcv: 2.0.0
```
Tips：以上dtk软件栈、python、torch、mmcv等DCU相关工具版本需要严格一一对应
2、其他非特殊库直接按照requirements.txt安装
```
cd /your_code_path/co-detr_mmcv
pip install mmdet -i https://mirrors.aliyun.com/pypi/simple/
```
## 数据集
COCO2017（在网络良好的情况下，如果没有下载数据集，程序会默认在线下载数据集）
[训练数据](http://images.cocodataset.org/zips/train2017.zip)
[验证数据](http://images.cocodataset.org/zips/val2017.zip)
[测试数据](http://images.cocodataset.org/zips/test2017.zip)
[标签数据](https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip)
数据集的目录结构如下：
```
├── images
│ ├── train2017
│ ├── val2017
│ ├── test2017
├── labels
│ ├── train2017
│ ├── val2017
├── annotations
│ ├── instances_val2017.json
├── LICENSE
├── README.txt
├── test-dev2017.txt
├── train2017.txt
├── val2017.txt
```
我们通过了mini数据集，供验证训练使用，如需正式使用，请下载完整COCO数据集或使用定制化数据集。
```bash
cd /your_code_path/co-detr_mmcv
cd datasets/
unzip coco_mini.zip
```
## 训练
- 数据集放置位置默认为当前目录下 datasets/
- 如需要变更数据集目录 请修改 [co_dino_5scale_r50_lsj_8xb2_1x_coco.py](projects%2FCO-DETR%2Fconfigs%2Fcodino%2Fco_dino_5scale_r50_lsj_8xb2_1x_coco.py)下的 data_root
### 单机单卡
```bash
cd /your_code_path/co-detr_mmcv
chmod -R 7777 train.sh
bash ./train.sh
```
### 单机四卡
```bash
cd /your_code_path/co-detr_mmcv
chmod -R 7777 train_multi.sh
bash ./train_multi.sh
```
## 推理
- 可使用官方模型权重进行推理，也可使用自己训练模型权重进行推理
- 这里以官方模型推理举例[[下载地址：co_dino_5scale_r50_lsj_8xb2_1x_coco-69a72d67.pth](https://download.openmmlab.com/mmdetection/v3.0/codetr/co_dino_5scale_r50_lsj_8xb2_1x_coco/co_dino_5scale_r50_lsj_8xb2_1x_coco-69a72d67.pth)]
```python
# 官方推理代码
python demo/image_demo.py demo/demo.jpg ./projects/CO-DETR/configs/codino/co_dino_5scale_r50_lsj_8xb2_1x_coco.py --weights co_dino_5scale_r50_lsj_8xb2_1x_coco-69a72d67.pth --device cuda
```
## result
![result.png](asserts%2Fresult.png)
### 精度
| 模型名称 | Backbone | Epochs | Aug | Dataset | amp混精 | Box AP |
|:-------:|:---------:|:------:|:---:|:-------:|:-----:|:------:|
| Co-detr | R50 | 12 | LSJ | COCO | on | 51.3 |
## 应用场景
### 算法类别
`目标检测`
### 热点应用行业
`金融,交通,教育`
## 源码仓库及问题反馈
- https://developer.hpccube.com/codes/modelzoo/co-detr_mmcv
## 参考资料
- https://github.com/open-mmlab/mmdetection/tree/main
README_zh-CN.md 0 → 100644
View file @ 4a950602
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<img src="resources/mmdet-logo.png" width="600"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab 官网</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab 开放平台</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div>&nbsp;</div>
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[📘使用文档](https://mmdetection.readthedocs.io/zh_CN/latest/) |
[🛠️安装教程](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html) |
[👀模型库](https://mmdetection.readthedocs.io/zh_CN/latest/model_zoo.html) |
[🆕更新日志](https://mmdetection.readthedocs.io/en/latest/notes/changelog.html) |
[🚀进行中的项目](https://github.com/open-mmlab/mmdetection/projects) |
[🤔报告问题](https://github.com/open-mmlab/mmdetection/issues/new/choose)
</div>
<div align="center">
[English](README.md) | 简体中文
</div>
<div align="center">
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<img src="https://github.com/open-mmlab/mmdetection/assets/17425982/6c29886f-ae7a-4a55-8be4-352ee85b7d3e"/>
</div>
## 简介
MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
主分支代码目前支持 PyTorch 1.8 及其以上的版本。
<img src="https://user-images.githubusercontent.com/12907710/187674113-2074d658-f2fb-42d1-ac15-9c4a695e64d7.png"/>
<details open>
<summary>主要特性</summary>
- **模块化设计**
MMDetection 将检测框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的检测模型
- **支持多种检测任务**
MMDetection 支持了各种不同的检测任务，包括**目标检测**，**实例分割**，**全景分割**，以及**半监督目标检测**。
- **速度快**
基本的框和 mask 操作都实现了 GPU 版本，训练速度比其他代码库更快或者相当，包括 [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) 和 [SimpleDet](https://github.com/TuSimple/simpledet)。
- **性能高**
MMDetection 这个算法库源自于 COCO 2018 目标检测竞赛的冠军团队 *MMDet* 团队开发的代码，我们在之后持续进行了改进和提升。
新发布的 [RTMDet](configs/rtmdet) 还在实时实例分割和旋转目标检测任务中取得了最先进的成果，同时也在目标检测模型中取得了最佳的的参数量和精度平衡。
</details>
除了 MMDetection 之外，我们还开源了深度学习训练库 [MMEngine](https://github.com/open-mmlab/mmengine) 和计算机视觉基础库 [MMCV](https://github.com/open-mmlab/mmcv)，它们是 MMDetection 的主要依赖。
## 最新进展
💎 **我们已经发布了 MM-Grounding-DINO Swin-B 和 Swin-L 预训练权重，欢迎试用和反馈.**
### 亮点
**v3.3.0** 版本已经在 2024.1.5 发布：
**MM-Grounding-DINO: 轻松涨点，数据到评测全面开源**
Grounding DINO 是一个统一了 2d 开放词汇目标检测和 Phrase Grounding 的检测预训练模型，应用广泛，但是其训练部分并未开源，为此提出了 MM-Grounding-DINO。其不仅作为 Grounding DINO 的开源复现版，MM-Grounding-DINO 基于重新构建的数据类型出发，在探索了不同数据集组合和初始化策略基础上实现了 Grounding DINO 的性能极大提升，并且从多个维度包括 OOD、REC、Phrase Grounding、OVD 和 Finetune 等方面进行评测，充分挖掘 Grounding 预训练优缺点，希望能为后续工作提供启发。
arxiv 技术报告：https://arxiv.org/abs/2401.02361
代码地址: [mm_grounding_dino/README.md](configs/mm_grounding_dino/README.md)
<div align=center>
<img src="https://github.com/open-mmlab/mmdetection/assets/17425982/fb14d1ee-5469-44d2-b865-aac9850c429c"/>
</div>
我们很高兴向大家介绍我们在实时目标识别任务方面的最新成果 RTMDet，包含了一系列的全卷积单阶段检测模型。 RTMDet 不仅在从 tiny 到 extra-large 尺寸的目标检测模型上实现了最佳的参数量和精度的平衡，而且在实时实例分割和旋转目标检测任务上取得了最先进的成果。 更多细节请参阅[技术报告](https://arxiv.org/abs/2212.07784)。 预训练模型可以在[这里](configs/rtmdet)找到。
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/real-time-instance-segmentation-on-mscoco)](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-dota-1)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-hrsc2016)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
| Task | Dataset | AP | FPS(TRT FP16 BS1 3090) |
| ------------------------ | ------- | ------------------------------------ | ---------------------- |
| Object Detection | COCO | 52.8 | 322 |
| Instance Segmentation | COCO | 44.6 | 188 |
| Rotated Object Detection | DOTA | 78.9(single-scale)/81.3(multi-scale) | 121 |
<div align=center>
<img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
</div>
## 安装
请参考[快速入门文档](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html)进行安装。
## 教程
请阅读[概述](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html)对 MMDetection 进行初步的了解。
为了帮助用户更进一步了解 MMDetection，我们准备了用户指南和进阶指南，请阅读我们的[文档](https://mmdetection.readthedocs.io/zh_CN/latest/)：
- 用户指南
<details>
- [训练 & 测试](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/index.html#train-test)
- [学习配置文件](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/config.html)
- [使用已有模型在标准数据集上进行推理](https://mmdetection.readthedocs.io/en/latest/user_guides/inference.html)
- [数据集准备](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/dataset_prepare.html)
- [测试现有模型](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/test.html)
- [在标准数据集上训练预定义的模型](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/train.html)
- [在自定义数据集上进行训练](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/train.html#train-with-customized-datasets)
- [在标准数据集上训练自定义模型](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/new_model.html)
- [模型微调](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/finetune.html)
- [提交测试结果](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/test_results_submission.html)
- [权重初始化](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/init_cfg.html)
- [将单阶段检测器作为 RPN](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/single_stage_as_rpn.html)
- [半监督目标检测](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/semi_det.html)
- [实用工具](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/index.html#useful-tools)
</details>
- 进阶指南
<details>
- [基础概念](https://mmdetection.readthedocs.io/zh_CN/latest/advanced_guides/index.html#basic-concepts)
- [组件定制](https://mmdetection.readthedocs.io/zh_CN/latest/advanced_guides/index.html#component-customization)
- [How to](https://mmdetection.readthedocs.io/zh_CN/latest/advanced_guides/index.html#how-to)
</details>
我们提供了检测的 colab 教程 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_Tutorial.ipynb) 和 实例分割的 colab 教程 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_Tutorial.ipynb)
同时，我们还提供了 [MMDetection 中文解读文案汇总](docs/zh_cn/article.md)
若需要将2.x版本的代码迁移至新版，请参考[迁移文档](https://mmdetection.readthedocs.io/en/latest/migration.html)。
## 基准测试和模型库
测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
<div align="center">
<b>算法架构</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Object Detection</b>
</td>
<td>
<b>Instance Segmentation</b>
</td>
<td>
<b>Panoptic Segmentation</b>
</td>
<td>
<b>Other</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<li><a href="configs/fast_rcnn">Fast R-CNN (ICCV'2015)</a></li>
<li><a href="configs/faster_rcnn">Faster R-CNN (NeurIPS'2015)</a></li>
<li><a href="configs/rpn">RPN (NeurIPS'2015)</a></li>
<li><a href="configs/ssd">SSD (ECCV'2016)</a></li>
<li><a href="configs/retinanet">RetinaNet (ICCV'2017)</a></li>
<li><a href="configs/cascade_rcnn">Cascade R-CNN (CVPR'2018)</a></li>
<li><a href="configs/yolo">YOLOv3 (ArXiv'2018)</a></li>
<li><a href="configs/cornernet">CornerNet (ECCV'2018)</a></li>
<li><a href="configs/grid_rcnn">Grid R-CNN (CVPR'2019)</a></li>
<li><a href="configs/guided_anchoring">Guided Anchoring (CVPR'2019)</a></li>
<li><a href="configs/fsaf">FSAF (CVPR'2019)</a></li>
<li><a href="configs/centernet">CenterNet (CVPR'2019)</a></li>
<li><a href="configs/libra_rcnn">Libra R-CNN (CVPR'2019)</a></li>
<li><a href="configs/tridentnet">TridentNet (ICCV'2019)</a></li>
<li><a href="configs/fcos">FCOS (ICCV'2019)</a></li>
<li><a href="configs/reppoints">RepPoints (ICCV'2019)</a></li>
<li><a href="configs/free_anchor">FreeAnchor (NeurIPS'2019)</a></li>
<li><a href="configs/cascade_rpn">CascadeRPN (NeurIPS'2019)</a></li>
<li><a href="configs/foveabox">Foveabox (TIP'2020)</a></li>
<li><a href="configs/double_heads">Double-Head R-CNN (CVPR'2020)</a></li>
<li><a href="configs/atss">ATSS (CVPR'2020)</a></li>
<li><a href="configs/nas_fcos">NAS-FCOS (CVPR'2020)</a></li>
<li><a href="configs/centripetalnet">CentripetalNet (CVPR'2020)</a></li>
<li><a href="configs/autoassign">AutoAssign (ArXiv'2020)</a></li>
<li><a href="configs/sabl">Side-Aware Boundary Localization (ECCV'2020)</a></li>
<li><a href="configs/dynamic_rcnn">Dynamic R-CNN (ECCV'2020)</a></li>
<li><a href="configs/detr">DETR (ECCV'2020)</a></li>
<li><a href="configs/paa">PAA (ECCV'2020)</a></li>
<li><a href="configs/vfnet">VarifocalNet (CVPR'2021)</a></li>
<li><a href="configs/sparse_rcnn">Sparse R-CNN (CVPR'2021)</a></li>
<li><a href="configs/yolof">YOLOF (CVPR'2021)</a></li>
<li><a href="configs/yolox">YOLOX (CVPR'2021)</a></li>
<li><a href="configs/deformable_detr">Deformable DETR (ICLR'2021)</a></li>
<li><a href="configs/tood">TOOD (ICCV'2021)</a></li>
<li><a href="configs/ddod">DDOD (ACM MM'2021)</a></li>
<li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
<li><a href="configs/conditional_detr">Conditional DETR (ICCV'2021)</a></li>
<li><a href="configs/dab_detr">DAB-DETR (ICLR'2022)</a></li>
<li><a href="configs/dino">DINO (ICLR'2023)</a></li>
<li><a href="configs/glip">GLIP (CVPR'2022)</a></li>
<li><a href="configs/ddq">DDQ (CVPR'2023)</a></li>
<li><a href="projects/DiffusionDet">DiffusionDet (ArXiv'2023)</a></li>
<li><a href="projects/EfficientDet">EfficientDet (CVPR'2020)</a></li>
<li><a href="projects/ViTDet">ViTDet (ECCV'2022)</a></li>
<li><a href="projects/Detic">Detic (ECCV'2022)</a></li>
<li><a href="projects/CO-DETR">CO-DETR (ICCV'2023)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/mask_rcnn">Mask R-CNN (ICCV'2017)</a></li>
<li><a href="configs/cascade_rcnn">Cascade Mask R-CNN (CVPR'2018)</a></li>
<li><a href="configs/ms_rcnn">Mask Scoring R-CNN (CVPR'2019)</a></li>
<li><a href="configs/htc">Hybrid Task Cascade (CVPR'2019)</a></li>
<li><a href="configs/yolact">YOLACT (ICCV'2019)</a></li>
<li><a href="configs/instaboost">InstaBoost (ICCV'2019)</a></li>
<li><a href="configs/solo">SOLO (ECCV'2020)</a></li>
<li><a href="configs/point_rend">PointRend (CVPR'2020)</a></li>
<li><a href="configs/detectors">DetectoRS (ArXiv'2020)</a></li>
<li><a href="configs/solov2">SOLOv2 (NeurIPS'2020)</a></li>
<li><a href="configs/scnet">SCNet (AAAI'2021)</a></li>
<li><a href="configs/queryinst">QueryInst (ICCV'2021)</a></li>
<li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
<li><a href="configs/condinst">CondInst (ECCV'2020)</a></li>
<li><a href="projects/SparseInst">SparseInst (CVPR'2022)</a></li>
<li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
<li><a href="configs/boxinst">BoxInst (CVPR'2021)</a></li>
<li><a href="projects/ConvNeXt-V2">ConvNeXt-V2 (Arxiv'2023)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/panoptic_fpn">Panoptic FPN (CVPR'2019)</a></li>
<li><a href="configs/maskformer">MaskFormer (NeurIPS'2021)</a></li>
<li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
<li><a href="configs/XDecoder">XDecoder (CVPR'2023)</a></li>
</ul>
</td>
<td>
</ul>
<li><b>Contrastive Learning</b></li>
<ul>
<ul>
<li><a href="configs/selfsup_pretrain">SwAV (NeurIPS'2020)</a></li>
<li><a href="configs/selfsup_pretrain">MoCo (CVPR'2020)</a></li>
<li><a href="configs/selfsup_pretrain">MoCov2 (ArXiv'2020)</a></li>
</ul>
</ul>
</ul>
<li><b>Distillation</b></li>
<ul>
<ul>
<li><a href="configs/ld">Localization Distillation (CVPR'2022)</a></li>
<li><a href="configs/lad">Label Assignment Distillation (WACV'2022)</a></li>
</ul>
</ul>
<li><b>Semi-Supervised Object Detection</b></li>
<ul>
<ul>
<li><a href="configs/soft_teacher">Soft Teacher (ICCV'2021)</a></li>
</ul>
</ul>
</ul>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
<div align="center">
<b>模块组件</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Backbones</b>
</td>
<td>
<b>Necks</b>
</td>
<td>
<b>Loss</b>
</td>
<td>
<b>Common</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<li>VGG (ICLR'2015)</li>
<li>ResNet (CVPR'2016)</li>
<li>ResNeXt (CVPR'2017)</li>
<li>MobileNetV2 (CVPR'2018)</li>
<li><a href="configs/hrnet">HRNet (CVPR'2019)</a></li>
<li><a href="configs/empirical_attention">Generalized Attention (ICCV'2019)</a></li>
<li><a href="configs/gcnet">GCNet (ICCVW'2019)</a></li>
<li><a href="configs/res2net">Res2Net (TPAMI'2020)</a></li>
<li><a href="configs/regnet">RegNet (CVPR'2020)</a></li>
<li><a href="configs/resnest">ResNeSt (ArXiv'2020)</a></li>
<li><a href="configs/pvt">PVT (ICCV'2021)</a></li>
<li><a href="configs/swin">Swin (CVPR'2021)</a></li>
<li><a href="configs/pvt">PVTv2 (ArXiv'2021)</a></li>
<li><a href="configs/resnet_strikes_back">ResNet strikes back (ArXiv'2021)</a></li>
<li><a href="configs/efficientnet">EfficientNet (ArXiv'2021)</a></li>
<li><a href="configs/convnext">ConvNeXt (CVPR'2022)</a></li>
<li><a href="projects/ConvNeXt-V2">ConvNeXtv2 (ArXiv'2023)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/pafpn">PAFPN (CVPR'2018)</a></li>
<li><a href="configs/nas_fpn">NAS-FPN (CVPR'2019)</a></li>
<li><a href="configs/carafe">CARAFE (ICCV'2019)</a></li>
<li><a href="configs/fpg">FPG (ArXiv'2020)</a></li>
<li><a href="configs/groie">GRoIE (ICPR'2020)</a></li>
<li><a href="configs/dyhead">DyHead (CVPR'2021)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/ghm">GHM (AAAI'2019)</a></li>
<li><a href="configs/gfl">Generalized Focal Loss (NeurIPS'2020)</a></li>
<li><a href="configs/seesaw_loss">Seasaw Loss (CVPR'2021)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py">OHEM (CVPR'2016)</a></li>
<li><a href="configs/gn">Group Normalization (ECCV'2018)</a></li>
<li><a href="configs/dcn">DCN (ICCV'2017)</a></li>
<li><a href="configs/dcnv2">DCNv2 (CVPR'2019)</a></li>
<li><a href="configs/gn+ws">Weight Standardization (ArXiv'2019)</a></li>
<li><a href="configs/pisa">Prime Sample Attention (CVPR'2020)</a></li>
<li><a href="configs/strong_baselines">Strong Baselines (CVPR'2021)</a></li>
<li><a href="configs/resnet_strikes_back">Resnet strikes back (ArXiv'2021)</a></li>
</ul>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
我们在[基于 MMDetection 的项目](./docs/zh_cn/notes/projects.md)中列举了一些其他的支持的算法。
## 常见问题
请参考 [FAQ](docs/zh_cn/notes/faq.md) 了解其他用户的常见问题。
## 贡献指南
我们感谢所有的贡献者为改进和提升 MMDetection 所作出的努力。我们将正在进行中的项目添加进了[GitHub Projects](https://github.com/open-mmlab/mmdetection/projects)页面，非常欢迎社区用户能参与进这些项目中来。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
## 致谢
MMDetection 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。
## 引用
如果你在研究中使用了本项目的代码或者性能基准，请参考如下 bibtex 引用 MMDetection。
```
@article{mmdetection,
title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
journal= {arXiv preprint arXiv:1906.07155},
year={2019}
}
```
## 开源许可证
该项目采用 [Apache 2.0 开源许可证](LICENSE)。
## OpenMMLab 的其他项目
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab 深度学习预训练工具箱
- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab 新一代人工智能内容生成（AIGC）工具箱
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱与测试基准
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
- [MIM](https://github.com/open-mmlab/mim): OpenMMlab 项目、算法、模型的统一入口
- [MMEval](https://github.com/open-mmlab/mmeval): 统一开放的跨框架算法评测库
- [Playground](https://github.com/open-mmlab/playground): 收集和展示 OpenMMLab 相关的前沿、有趣的社区项目
## 欢迎加入 OpenMMLab 社区
扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，扫描下方微信二维码添加喵喵好友，进入 MMDectection 微信交流社群。【加好友申请格式：研究方向+地区+学校/公司+姓名】
<div align="center">
<img src="resources/zhihu_qrcode.jpg" height="400" /> <img src="resources/miaomiao_qrcode.jpg" height="400" />
</div>
我们会在 OpenMMLab 社区为大家
- 📢 分享 AI 框架的前沿核心技术
- 💻 解读 PyTorch 常用模块源码
- 📰 发布 OpenMMLab 的相关新闻
- 🚀 介绍 OpenMMLab 开发的前沿算法
- 🏃 获取更高效的问题答疑和意见反馈
- 🔥 提供与各行各业开发者充分交流的平台
干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
configs/_base_/datasets/ade20k_instance.py 0 → 100644
View file @ 4a950602
# dataset settings
dataset_type = 'ADE20KInstanceDataset'
data_root = 'data/ADEChallengeData2016/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/ADEChallengeData2016/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(2560, 640), keep_ratio=True),
# If you don't have a gt annotation, delete the pipeline
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='ade20k_instance_val.json',
data_prefix=dict(img='images/validation'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'ade20k_instance_val.json',
metric=['bbox', 'segm'],
format_only=False,
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/ade20k_panoptic.py 0 → 100644
View file @ 4a950602
# dataset settings
dataset_type = 'ADE20KPanopticDataset'
data_root = 'data/ADEChallengeData2016/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(2560, 640), keep_ratio=True),
dict(type='LoadPanopticAnnotations', backend_args=backend_args),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
val_dataloader = dict(
batch_size=1,
num_workers=0,
persistent_workers=False,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='ade20k_panoptic_val.json',
data_prefix=dict(img='images/validation/', seg='ade20k_panoptic_val/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoPanopticMetric',
ann_file=data_root + 'ade20k_panoptic_val.json',
seg_prefix=data_root + 'ade20k_panoptic_val/',
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/ade20k_semantic.py 0 → 100644
View file @ 4a950602
dataset_type = 'ADE20KSegDataset'
data_root = 'data/ADEChallengeData2016/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/ADEChallengeData2016/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(2048, 512), keep_ratio=True),
dict(
type='LoadAnnotations',
with_bbox=False,
with_mask=False,
with_seg=True,
reduce_zero_label=True),
dict(
type='PackDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape'))
]
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
img_path='images/validation',
seg_map_path='annotations/validation'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='SemSegMetric', iou_metrics=['mIoU'])
test_evaluator = val_evaluator
configs/_base_/datasets/cityscapes_detection.py 0 → 100644
View file @ 4a950602
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/segmentation/cityscapes/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/segmentation/',
# 'data/': 's3://openmmlab/datasets/segmentation/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=[(2048, 800), (2048, 1024)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
# If you don't have a gt annotation, delete the pipeline
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='RepeatDataset',
times=8,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instancesonly_filtered_gtFine_train.json',
data_prefix=dict(img='leftImg8bit/train/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instancesonly_filtered_gtFine_val.json',
data_prefix=dict(img='leftImg8bit/val/'),
test_mode=True,
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/instancesonly_filtered_gtFine_val.json',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/cityscapes_instance.py 0 → 100644
View file @ 4a950602
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/segmentation/cityscapes/'
# Method 2: Use backend_args, file_client_args in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/segmentation/',
# 'data/': 's3://openmmlab/datasets/segmentation/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomResize',
scale=[(2048, 800), (2048, 1024)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
# If you don't have a gt annotation, delete the pipeline
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='RepeatDataset',
times=8,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instancesonly_filtered_gtFine_train.json',
data_prefix=dict(img='leftImg8bit/train/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instancesonly_filtered_gtFine_val.json',
data_prefix=dict(img='leftImg8bit/val/'),
test_mode=True,
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = [
dict(
type='CocoMetric',
ann_file=data_root +
'annotations/instancesonly_filtered_gtFine_val.json',
metric=['bbox', 'segm'],
backend_args=backend_args),
dict(
type='CityScapesMetric',
seg_prefix=data_root + 'gtFine/val',
outfile_prefix='./work_dirs/cityscapes_metric/instance',
backend_args=backend_args)
]
test_evaluator = val_evaluator
# inference on test dataset and
# format the output results for submission.
# test_dataloader = dict(
# batch_size=1,
# num_workers=2,
# persistent_workers=True,
# drop_last=False,
# sampler=dict(type='DefaultSampler', shuffle=False),
# dataset=dict(
# type=dataset_type,
# data_root=data_root,
# ann_file='annotations/instancesonly_filtered_gtFine_test.json',
# data_prefix=dict(img='leftImg8bit/test/'),
# test_mode=True,
# filter_cfg=dict(filter_empty_gt=True, min_size=32),
# pipeline=test_pipeline))
# test_evaluator = dict(
# type='CityScapesMetric',
# format_only=True,
# outfile_prefix='./work_dirs/cityscapes_metric/test')
configs/_base_/datasets/coco_caption.py 0 → 100644
View file @ 4a950602
# data settings
dataset_type = 'CocoCaptionDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
test_pipeline = [
dict(
type='LoadImageFromFile',
imdecode_backend='pillow',
backend_args=backend_args),
dict(
type='Resize',
scale=(224, 224),
interpolation='bicubic',
backend='pillow'),
dict(type='PackInputs', meta_keys=['image_id']),
]
# ann_file download from
# train dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json # noqa
# val dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json # noqa
# test dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json # noqa
# val evaluator: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json # noqa
# test evaluator: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json # noqa
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/coco_karpathy_val.json',
pipeline=test_pipeline,
))
val_evaluator = dict(
type='COCOCaptionMetric',
ann_file=data_root + 'annotations/coco_karpathy_val_gt.json',
)
# # If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator
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