mirror of https://github.com/open-mmlab/mmyolo.git
61 lines
2.5 KiB
Markdown
61 lines
2.5 KiB
Markdown
# Prerequisites
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Compatible MMEngine, MMCV and MMDetection versions are shown as below. Please install the correct version to avoid installation issues.
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| MMYOLO version | MMDetection version | MMEngine version | MMCV version |
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| :------------: | :----------------------: | :----------------------: | :---------------------: |
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| main | mmdet>=3.0.0, \<3.1.0 | mmengine>=0.7.1, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 |
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| 0.6.0 | mmdet>=3.0.0, \<3.1.0 | mmengine>=0.7.1, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 |
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| 0.5.0 | mmdet>=3.0.0rc6, \<3.1.0 | mmengine>=0.6.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 |
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| 0.4.0 | mmdet>=3.0.0rc5, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
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| 0.3.0 | mmdet>=3.0.0rc5, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
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| 0.2.0 | mmdet>=3.0.0rc3, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
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| 0.1.3 | mmdet>=3.0.0rc3, \<3.1.0 | mmengine>=0.3.1, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
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| 0.1.2 | mmdet>=3.0.0rc2, \<3.1.0 | mmengine>=0.3.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 |
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| 0.1.1 | mmdet==3.0.0rc1 | mmengine>=0.1.0, \<0.2.0 | mmcv>=2.0.0rc0, \<2.1.0 |
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| 0.1.0 | mmdet==3.0.0rc0 | mmengine>=0.1.0, \<0.2.0 | mmcv>=2.0.0rc0, \<2.1.0 |
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In this section, we demonstrate how to prepare an environment with PyTorch.
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MMDetection works on Linux, Windows, and macOS. It requires:
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- Python 3.7+
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- PyTorch 1.7+
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- CUDA 9.2+
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- GCC 5.4+
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```{note}
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If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](#installation). Otherwise, you can follow these steps for the preparation.
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```
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**Step 0.** Download and install Miniconda from the [official website](https://docs.conda.io/en/latest/miniconda.html).
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**Step 1.** Create a conda environment and activate it.
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```shell
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conda create --name openmmlab python=3.8 -y
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conda activate openmmlab
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```
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**Step 2.** Install PyTorch following [official commands](https://pytorch.org/get-started/locally/), e.g.
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On GPU platforms:
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```shell
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conda install pytorch torchvision -c pytorch
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```
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On CPU platforms:
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```shell
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conda install pytorch torchvision cpuonly -c pytorch
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```
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**Step 3.** Verify PyTorch installation
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```shell
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python -c "import torch; print(torch.__version__); print(torch.cuda.is_available())"
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```
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If the GPU is used, the version information and `True` are printed; otherwise, the version information and `False` are printed.
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