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Bump version to 0.6.0
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23 changes: 17 additions & 6 deletions .circleci/test.yml
Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ jobs:
command: |
pip install -U openmim
mim install git+https://github.com/open-mmlab/mmengine.git@main
mim install 'mmcv >= 2.0.0rc4'
mim install 'mmcv >= 2.0.0'
mim install git+https://github.com/open-mmlab/[email protected]
pip install -r requirements/albu.txt
pip install -r requirements/tests.txt
Expand All @@ -90,16 +90,17 @@ jobs:
name: Run unittests
command: |
export LD_LIBRARY_PATH=/home/circleci/project/onnxruntime-linux-x64-1.8.1/lib:${LD_LIBRARY_PATH}
coverage run --branch --source mmyolo -m pytest tests/
coverage xml
coverage report -m
pytest tests/
# coverage run --branch --source mmyolo -m pytest tests/
# coverage xml
# coverage report -m
build_cuda:
parameters:
torch:
type: string
cuda:
type: enum
enum: ["10.1", "10.2", "11.1", "11.0"]
enum: ["10.1", "10.2", "11.0", "11.7"]
cudnn:
type: integer
default: 7
Expand All @@ -125,7 +126,7 @@ jobs:
command: |
docker exec mmyolo pip install -U openmim
docker exec mmyolo mim install -e /mmengine
docker exec mmyolo mim install 'mmcv >= 2.0.0rc4'
docker exec mmyolo mim install 'mmcv >= 2.0.0'
docker exec mmyolo pip install -e /mmdetection
docker exec mmyolo pip install -r requirements/albu.txt
docker exec mmyolo pip install -r requirements/tests.txt
Expand Down Expand Up @@ -168,6 +169,9 @@ workflows:
- lint
- build_cpu:
name: maximum_version_cpu
# mmdeploy not supported
# torch: 2.0.0
# torchvision: 0.15.1
torch: 1.12.1
torchvision: 0.13.1
python: 3.9.0
Expand All @@ -185,6 +189,13 @@ workflows:
cuda: "10.2"
requires:
- hold
- build_cuda:
name: maximum_version_gpu
torch: 2.0.0
cuda: "11.7"
cudnn: 8
requires:
- hold
merge_stage_test:
when:
not: << pipeline.parameters.lint_only >>
Expand Down
1 change: 1 addition & 0 deletions .dev_scripts/gather_models.py
Original file line number Diff line number Diff line change
Expand Up @@ -108,6 +108,7 @@ def get_dataset_name(config):
name_map = dict(
CityscapesDataset='Cityscapes',
CocoDataset='COCO',
PoseCocoDataset='COCO Person',
YOLOv5CocoDataset='COCO',
CocoPanopticDataset='COCO',
YOLOv5DOTADataset='DOTA 1.0',
Expand Down
31 changes: 16 additions & 15 deletions README.md
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Expand Up @@ -77,17 +77,13 @@ English | [简体中文](README_zh-CN.md)

## 🥳 🚀 What's New [🔝](#-table-of-contents)

💎 **v0.5.0** was released on 2/3/2023:

1. Support [RTMDet-R](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/README.md#rotated-object-detection) rotated object detection
2. Support for using mask annotation to improve [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov8/README.md) object detection performance
3. Support [MMRazor](https://github.com/open-mmlab/mmyolo/blob/dev/configs/razor/subnets/README.md) searchable NAS sub-network as the backbone of YOLO series algorithm
4. Support calling [MMRazor](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/distillation/README.md) to distill the knowledge of RTMDet
5. [MMYOLO](https://mmyolo.readthedocs.io/zh_CN/dev/) document structure optimization, comprehensive content upgrade
6. Improve YOLOX mAP and training speed based on RTMDet training hyperparameters
7. Support calculation of model parameters and FLOPs, provide GPU latency data on T4 devices, and update [Model Zoo](https://github.com/open-mmlab/mmyolo/blob/dev/docs/en/model_zoo.md)
8. Support test-time augmentation (TTA)
9. Support RTMDet, YOLOv8 and YOLOv7 assigner visualization
💎 **v0.6.0** was released on 15/8/2023:

- Support YOLOv5 instance segmentation
- Support YOLOX-Pose based on MMPose
- Add 15 minutes instance segmentation tutorial.
- YOLOv5 supports using mask annotation to optimize bbox
- Add Multi-scale training and testing docs

For release history and update details, please refer to [changelog](https://mmyolo.readthedocs.io/en/latest/notes/changelog.html).

Expand Down Expand Up @@ -150,7 +146,7 @@ conda activate mmyolo
pip install openmim
mim install "mmengine>=0.6.0"
mim install "mmcv>=2.0.0rc4,<2.1.0"
mim install "mmdet>=3.0.0rc6,<3.1.0"
mim install "mmdet>=3.0.0,<4.0.0"
git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
# Install albumentations
Expand Down Expand Up @@ -184,6 +180,7 @@ For different parts from MMDetection, we have also prepared user guides and adva
<summary>Recommended Topics</summary>

- [How to contribute code to MMYOLO](docs/en/recommended_topics/contributing.md)
- [Training testing tricks](docs/en/recommended_topics/training_testing_tricks.md)
- [MMYOLO model design](docs/en/recommended_topics/model_design.md)
- [Algorithm principles and implementation](docs/en/recommended_topics/algorithm_descriptions/)
- [Replace the backbone network](docs/en/recommended_topics/replace_backbone.md)
Expand All @@ -192,7 +189,7 @@ For different parts from MMDetection, we have also prepared user guides and adva
- [Visualization](docs/en/recommended_topics/visualization.md)
- [Model deployment](docs/en/recommended_topics/deploy/)
- [Troubleshooting steps](docs/en/recommended_topics/troubleshooting_steps.md)
- [MMYOLO industry examples](docs/en/recommended_topics/industry_examples.md)
- [MMYOLO application examples](docs/en/recommended_topics/application_examples/)
- [MM series repo essential basics](docs/en/recommended_topics/mm_basics.md)
- [Dataset preparation and description](docs/en/recommended_topics/dataset_preparation.md)

Expand All @@ -204,6 +201,7 @@ For different parts from MMDetection, we have also prepared user guides and adva
- [Resume training](docs/en/common_usage/resume_training.md)
- [Enabling and disabling SyncBatchNorm](docs/en/common_usage/syncbn.md)
- [Enabling AMP](docs/en/common_usage/amp_training.md)
- [Multi-scale training and testing](docs/en/common_usage/ms_training_testing.md)
- [TTA Related Notes](docs/en/common_usage/tta.md)
- [Add plugins to the backbone network](docs/en/common_usage/plugins.md)
- [Freeze layers](docs/en/common_usage/freeze_layers.md)
Expand Down Expand Up @@ -283,6 +281,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
<summary><b>Supported Algorithms</b></summary>

- [x] [YOLOv5](configs/yolov5)
- [ ] [YOLOv5u](configs/yolov5/yolov5u) (Inference only)
- [x] [YOLOX](configs/yolox)
- [x] [RTMDet](configs/rtmdet)
- [x] [RTMDet-Rotated](configs/rtmdet)
Expand Down Expand Up @@ -405,8 +404,8 @@ This project is released under the [GPL 3.0 license](LICENSE).

- [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.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
- [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.
Expand All @@ -424,4 +423,6 @@ This project is released under the [GPL 3.0 license](LICENSE).
- [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): OpenMMLab machine learning evaluation library.
- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.
35 changes: 18 additions & 17 deletions README_zh-CN.md
Original file line number Diff line number Diff line change
Expand Up @@ -78,17 +78,13 @@

## 🥳 🚀 最新进展 [🔝](#-table-of-contents)

💎 **v0.5.0** 版本已经在 2023.3.2 发布:

1. 支持了 [RTMDet-R](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/README.md#rotated-object-detection) 旋转框目标检测任务和算法
2. [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov8/README.md) 支持使用 mask 标注提升目标检测模型性能
3. 支持 [MMRazor](https://github.com/open-mmlab/mmyolo/blob/dev/configs/razor/subnets/README.md) 搜索的 NAS 子网络作为 YOLO 系列算法的 backbone
4. 支持调用 [MMRazor](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/distillation/README.md) 对 RTMDet 进行知识蒸馏
5. [MMYOLO](https://mmyolo.readthedocs.io/zh_CN/dev/) 文档结构优化,内容全面升级
6. 基于 RTMDet 训练超参提升 YOLOX 精度和训练速度
7. 支持模型参数量、FLOPs 计算和提供 T4 设备上 GPU 延时数据,并更新了 [Model Zoo](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/model_zoo.md)
8. 支持测试时增强 TTA
9. 支持 RTMDet、YOLOv8 和 YOLOv7 assigner 可视化
💎 **v0.6.0** 版本已经在 2023.8.15 发布:

- 支持 YOLOv5 实例分割
- 基于 MMPose 支持 YOLOX-Pose
- 添加 15 分钟的实例分割教程
- YOLOv5 支持使用 mask 标注来优化边界框
- 添加多尺度训练和测试文档

我们提供了实用的**脚本命令速查表**

Expand All @@ -108,7 +104,7 @@
| 🌟 | 自定义数据集从标注到部署保姆级教程 | [![Link](https://i2.hdslb.com/bfs/archive/13f566c89a18c9c881713b63ec14da952d4c0b14.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1RG4y137i5) [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1RG4y137i5)](https://www.bilibili.com/video/BV1JG4y1d7GC) | [自定义数据集从标注到部署保姆级教程](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/user_guides/custom_dataset.md) |
| 🌟 | 顶会第一步 · 模块自定义 | [![Link](http://i2.hdslb.com/bfs/archive/5b23d41ac57466824eaf185ef806ef734414e93b.jpg@112w_63h_1c.webp)](https://www.bilibili.com/video/BV1yd4y1j7VD) [![bilibili](https://img.shields.io/badge/dynamic/json?label=views&style=social&logo=bilibili&query=data.stat.view&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Fweb-interface%2Fview%3Fbvid%3DBV1yd4y1j7VD)](https://www.bilibili.com/video/BV1yd4y1j7VD) | [顶会第一步·模块自定义.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[实用类第四期]顶会第一步·模块自定义.ipynb) |

完整视频列表请参考 [资源汇总页面](https://mmyolo.readthedocs.io/zh_CN/latest/article.html)
完整视频列表请参考 [中文解读资源汇总 - 视频](https://mmyolo.readthedocs.io/zh_CN/latest/get_started/article.html)

发布历史和更新细节请参考 [更新日志](https://mmyolo.readthedocs.io/zh_CN/latest/notes/changelog.html)

Expand Down Expand Up @@ -171,7 +167,7 @@ conda activate mmyolo
pip install openmim
mim install "mmengine>=0.6.0"
mim install "mmcv>=2.0.0rc4,<2.1.0"
mim install "mmdet>=3.0.0rc6,<3.1.0"
mim install "mmdet>=3.0.0,<4.0.0"
git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
# Install albumentations
Expand Down Expand Up @@ -205,6 +201,7 @@ MMYOLO 用法和 MMDetection 几乎一致,所有教程都是通用的,你也
<summary>推荐专题</summary>

- [如何给 MMYOLO 贡献代码](docs/zh_cn/recommended_topics/contributing.md)
- [训练和测试技巧](docs/zh_cn/recommended_topics/training_testing_tricks.md)
- [MMYOLO 模型结构设计](docs/zh_cn/recommended_topics/model_design.md)
- [原理和实现全解析](docs/zh_cn/recommended_topics/algorithm_descriptions/)
- [轻松更换主干网络](docs/zh_cn/recommended_topics/replace_backbone.md)
Expand All @@ -213,7 +210,7 @@ MMYOLO 用法和 MMDetection 几乎一致,所有教程都是通用的,你也
- [关于可视化的一切](docs/zh_cn/recommended_topics/visualization.md)
- [模型部署流程](docs/zh_cn/recommended_topics/deploy/)
- [常见错误排查步骤](docs/zh_cn/recommended_topics/troubleshooting_steps.md)
- [MMYOLO 产业范例介绍](docs/zh_cn/recommended_topics/industry_examples.md)
- [MMYOLO 应用范例介绍](docs/zh_cn/recommended_topics/application_examples/)
- [MM 系列 Repo 必备基础](docs/zh_cn/recommended_topics/mm_basics.md)
- [数据集准备和说明](docs/zh_cn/recommended_topics/dataset_preparation.md)

Expand All @@ -225,9 +222,10 @@ MMYOLO 用法和 MMDetection 几乎一致,所有教程都是通用的,你也
- [恢复训练](docs/zh_cn/common_usage/resume_training.md)
- [开启和关闭 SyncBatchNorm](docs/zh_cn/common_usage/syncbn.md)
- [开启混合精度训练](docs/zh_cn/common_usage/amp_training.md)
- [多尺度训练和测试](docs/zh_cn/common_usage/ms_training_testing.md)
- [测试时增强相关说明](docs/zh_cn/common_usage/tta.md)
- [给主干网络增加插件](docs/zh_cn/common_usage/plugins.md)
- [冻结指定网络层权重](docs/zh_cn/common_usage/common_usage/freeze_layers.md)
- [冻结指定网络层权重](docs/zh_cn/common_usage/freeze_layers.md)
- [输出模型预测结果](docs/zh_cn/common_usage/output_predictions.md)
- [设置随机种子](docs/zh_cn/common_usage/set_random_seed.md)
- [算法组合替换教程](docs/zh_cn/common_usage/module_combination.md)
Expand Down Expand Up @@ -305,6 +303,7 @@ MMYOLO 用法和 MMDetection 几乎一致,所有教程都是通用的,你也
<summary><b>支持的算法</b></summary>

- [x] [YOLOv5](configs/yolov5)
- [ ] [YOLOv5u](configs/yolov5/yolov5u) (仅推理)
- [x] [YOLOX](configs/yolox)
- [x] [RTMDet](configs/rtmdet)
- [x] [RTMDet-Rotated](configs/rtmdet)
Expand Down Expand Up @@ -426,8 +425,8 @@ MMYOLO 是一款由来自不同高校和企业的研发人员共同参与贡献

- [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
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
- [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
Expand All @@ -445,7 +444,9 @@ MMYOLO 是一款由来自不同高校和企业的研发人员共同参与贡献
- [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): MIM 是 OpenMMlab 项目、算法、模型的统一入口
- [MMEval](https://github.com/open-mmlab/mmeval): OpenMMLab 机器学习算法评测库
- [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 [🔝](#-table-of-contents)

Expand Down
23 changes: 16 additions & 7 deletions configs/_base_/default_runtime.py
Original file line number Diff line number Diff line change
Expand Up @@ -25,10 +25,19 @@
load_from = None
resume = False

# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
# 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 MMDet 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))

backend_args = None
9 changes: 5 additions & 4 deletions configs/_base_/det_p5_tta.py
Original file line number Diff line number Diff line change
@@ -1,11 +1,12 @@
# TODO: Need to solve the problem of multiple file_client_args parameters
# _file_client_args = dict(
# TODO: Need to solve the problem of multiple backend_args parameters
# _backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
_file_client_args = dict(backend='disk')

_backend_args = None

tta_model = dict(
type='mmdet.DetTTAModel',
Expand Down Expand Up @@ -37,7 +38,7 @@
]

tta_pipeline = [
dict(type='LoadImageFromFile', file_client_args=_file_client_args),
dict(type='LoadImageFromFile', backend_args=_backend_args),
dict(
type='TestTimeAug',
transforms=[
Expand Down
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