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v3.0.0rc0

open-mmlab/mmdetection

版本发布时间: 2022-09-01 11:57:50

open-mmlab/mmdetection最新发布版本:v3.3.0(2024-01-05 14:24:15)

We are excited to announce the release of MMDetection 3.0.0rc0. MMDet 3.0.0rc0 is the first version of MMDetection 3.x, a part of the OpenMMLab 2.0 projects. Built upon the new training engine, MMDet 3.x unifies the interfaces of the dataset, models, evaluation, and visualization with faster training and testing speed. It also provides a general semi-supervised object detection framework and strong baselines.

Highlights

  1. New engine. MMDet 3.x is based on MMEngine, which provides a universal and powerful runner that allows more flexible customizations and significantly simplifies the entry points of high-level interfaces.

  2. Unified interfaces. As a part of the OpenMMLab 2.0 projects, MMDet 3.x unifies and refactors the interfaces and internal logic of training, testing, datasets, models, evaluation, and visualization. All the OpenMMLab 2.0 projects share the same design in those interfaces and logic to allow the emergence of multi-task/modality algorithms.

  3. Faster speed. We optimize the training and inference speed for common models and configurations, achieving faster or similar speed in comparison with Detection2. Model details of benchmark will be updated in this note.

  4. General semi-supervised object detection. Benefitting from the unified interfaces, we support a general semi-supervised learning framework that works with all the object detectors supported in MMDet 3.x. Please refer to semi-supervised object detection for details.

  5. Strong baselines. We release strong baselines of many popular models to enable fair comparisons among state-of-the-art models.

  6. New features and algorithms:

  7. More documentation and tutorials. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it here.

Breaking Changes

MMDet 3.x has gone through big changes to have better design, higher efficiency, more flexibility, and more unified interfaces. Besides the changes in API, we briefly list the major breaking changes in this section. We will update the migration guide to provide complete details and migration instructions. Users can also refer to the API doc for more details.

Dependencies

Training and testing

Configs

Dataset

The Dataset classes implemented in MMDet 3.x all inherit from the BaseDetDataset, which inherits from the BaseDataset in MMEngine. In addition to the changes in interfaces, there are several changes in Dataset in MMDet 3.x.

Data Transforms

The data transforms in MMDet 3.x all inherits from BaseTransform in MMCV>=2.0.0rc0, which defines a new convention in OpenMMLab 2.0 projects. Besides the interface changes, there are several changes listed below:

Model

The models in MMDet 3.x all inherit from BaseModel in MMEngine, which defines a new convention of models in OpenMMLab 2.0 projects. Users can refer to the tutorial of the model in MMengine for more details. Accordingly, there are several changes as the following:

Evaluation

The evaluation in MMDet 2.x strictly binds with the dataset. In contrast, MMDet 3.x decomposes the evaluation from the dataset so that all the detection datasets can evaluate with COCO AP and other metrics implemented in MMDet 3.x. MMDet 3.x mainly implements corresponding metrics for each dataset, which are manipulated by Evaluator to complete the evaluation. Users can build an evaluator in MMDet 3.x to conduct offline evaluation, i.e., evaluate predictions that may not produce in MMDet 3.x with the dataset as long as the dataset and the prediction follow the dataset conventions. More details can be found in the tutorial in mmengine.

Visualization

The functions of visualization in MMDet 2.x are removed. Instead, in OpenMMLab 2.0 projects, we use Visualizer to visualize data. MMDet 3.x implements DetLocalVisualizer to allow visualization of ground truths, model predictions, feature maps, etc., at any place. It also supports sending the visualization data to any external visualization backends such as Tensorboard.

Improvements

Bug Fixes

New Features

  1. Support a general semi-supervised learning framework that works with all the object detectors supported in MMDet 3.x. Please refer to semi-supervised object detection for details.
  2. Enable all the single-stage detectors to serve as region proposal networks. We give an example of using FCOS as RPN.
  3. Support a semi-supervised object detection algorithm: SoftTeacher.
  4. Support the updated CenterNet.
  5. Support data structures HorizontalBoxes and BaseBoxes to encapsulate different kinds of bounding boxes. We are migrating to use data structures of boxes to replace the use of pure tensor boxes. This will unify the usages of different kinds of bounding boxes in MMDet 3.x and MMRotate 1.x to simplify the implementation and reduce redundant codes.

Planned changes

We list several planned changes of MMDet 3.0.0rc0 so that the community could more comprehensively know the progress of MMDet 3.x. Feel free to create a PR, issue, or discussion if you are interested, have any suggestions and feedback, or want to participate.

  1. Test-time augmentation: which is supported in MMDet 2.x, is not implemented in this version due to the limited time slot. We will support it in the following releases with a new and simplified design.
  2. Inference interfaces: unified inference interfaces will be supported in the future to ease the use of released models.
  3. Interfaces of useful tools that can be used in Jupyter Notebook or Colab: more useful tools that are implemented in the tools directory will have their python interfaces so that they can be used in Jupyter Notebook, Colab, and downstream libraries.
  4. Documentation: we will add more design docs, tutorials, and migration guidance so that the community can deep dive into our new design, participate the future development, and smoothly migrate downstream libraries to MMDet 3.x.
  5. Wandb visualization: MMDet 2.x supports data visualization since v2.25.0, which has not been migrated to MMDet 3.x for now. Since WandB provides strong visualization and experiment management capabilities, a DetWandbVisualizer and maybe a hook are planned to fully migrate those functionalities from MMDet 2.x.
  6. Full support of WiderFace dataset (#8508) and Fast R-CNN: we are verifying their functionalities and will fix related issues soon.
  7. Migrate DETR-series algorithms (#8655, #8533) and YOLOv3 on IPU (#8552) from MMDet 2.x.

Contributors

A total of 11 developers contributed to this release. Thanks @shuxp, @wanghonglie, @Czm369, @BIGWangYuDong, @zytx121, @jbwang1997, @chhluo, @jshilong, @RangiLyu, @hhaAndroid, @ZwwWayne

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.25.0...v3.0.0rc0

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