In addition, we have preliminarily supported several new models on the [v1.0.0.dev0](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0) branch, including [DGCNN](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/configs/dgcnn/README.md), [SMOKE](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/configs/smoke/README.md) and [PGD](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/configs/pgd/README.md).
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@@ -48,11 +48,11 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
-**Natural integration with 2D detection**
All the about **300+ models, methods of 40+ papers**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
All the about **300+ models, methods of 40+ papers**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.
-**High efficiency**
It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
@@ -70,15 +70,15 @@ This project is released under the [Apache 2.0 license](LICENSE).
## Changelog
v0.17.2 was released in 1/11/2021.
Please refer to [changelog.md](docs/changelog.md) for details and release history.
v0.17.3 was released in 1/12/2021.
Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
For branch v1.0.0.dev0, please refer to [changelog_v1.0.md](https://github.com/Tai-Wang/mmdetection3d/blob/v1.0.0.dev0-changelog/docs/changelog_v1.0.md) for our latest features and more details.
## Benchmark and model zoo
Supported methods and backbones are shown in the below table.
Results and models are available in the [model zoo](docs/model_zoo.md).
Results and models are available in the [model zoo](docs/en/model_zoo.md).
**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.
## Installation
Please refer to [getting_started.md](docs/getting_started.md) for installation.
Please refer to [getting_started.md](docs/en/getting_started.md) for installation.
## Get Started
Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/1_exist_data_model.md) and [with customized dataset](docs/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/tutorials/config.md), [adding new dataset](docs/tutorials/customize_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing models](docs/tutorials/customize_models.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [Waymo dataset](docs/datasets/waymo_det.md).
Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/1_exist_data_model.md) and [with customized dataset](docs/en/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/tutorials/config.md), [adding new dataset](docs/en/tutorials/customize_dataset.md), [designing data pipeline](docs/en/tutorials/data_pipeline.md), [customizing models](docs/en/tutorials/customize_models.md), [customizing runtime settings](docs/en/tutorials/customize_runtime.md) and [Waymo dataset](docs/en/datasets/waymo_det.md).
Please refer to [FAQ](docs/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/compatibility.md) to be aware of the BC-breaking updates introduced in each version.
Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/en/compatibility.md) to be aware of the BC-breaking updates introduced in each version.
## Citation
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@@ -183,3 +183,6 @@ We wish that the toolbox and benchmark could serve the growing research communit
-[MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
-[MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition and understanding toolbox.
-[MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
-[MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
-[MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
-[MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- Change the default show value to `False` in show_result function to avoid unnecessary errors (#1034)
- Improve the visualization of detection results with colorized points in [single_gpu_test](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/apis/test.py#L11)(#1050)
- Clean unnecessary custom_imports in entrypoints (#1068)
#### Bug Fixes
- Update mmcv version in the Dockerfile (#1036)
- Fix the memory-leak problem when loading checkpoints in [init_model](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/apis/inference.py#L36)(#1045)
- Fix incorrect velocity indexing when formatting boxes on nuScenes (#1049)
- Explicitly set cuda device ID in [init_model](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/apis/inference.py#L36) to avoid memory allocation on unexpected devices (#1056)
