• Linux | Windows | macOS

  • Python 3.7+

  • PyTorch 1.5+

  • CUDA 9.2+

  • GCC 5+

  • mmcv 1.3.12+

  • mmdet 2.16.0+

  • mmcls 0.15.0+

Compatible MMCV, MMClassification and MMDetection versions are shown as below. Please install the correct version of them to avoid installation issues.

MMFewShot version MMCV version MMClassification version MMDetection version
master mmcv-full>=1.3.12 mmdet >= 2.16.0 mmcls >=0.15.0

Note: You need to run pip uninstall mmcv first if you have mmcv installed. If mmcv and mmcv-full are both installed, there will be ModuleNotFoundError.


A from-scratch setup script

Assuming that you already have CUDA 10.1 installed, here is a full script for setting up MMFewShot with conda. You can refer to the step-by-step installation instructions in the next section.

conda create -n openmmlab python=3.7 -y
conda activate openmmlab

conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.1 -c pytorch

pip install openmim
mim install mmcv-full

# install mmclassification mmdetection
mim install mmcls
mim install mmdet

# install mmfewshot
git clone
cd mmfewshot
pip install -r requirements/build.txt
pip install -v -e .  # or "python develop"

Prepare environment

  1. Create a conda virtual environment and activate it.

    conda create -n openmmlab python=3.7 -y
    conda activate openmmlab
  2. Install PyTorch and torchvision following the official instructions, e.g.,

    conda install pytorch torchvision -c pytorch

    Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.

    E.g If you have CUDA 10.1 installed under /usr/local/cuda and would like to install PyTorch 1.7, you need to install the prebuilt PyTorch with CUDA 10.1.

    conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.1 -c pytorch

Install MMFewShot

It is recommended to install MMFewShot with MIM, which automatically handle the dependencies of OpenMMLab projects, including mmcv and other python packages.

pip install openmim
mim install mmfewshot

Or you can still install MMFewShot manually:

  1. Install mmcv-full.

    # pip install mmcv-full -f{cu_version}/{torch_version}/index.html
    pip install mmcv-full -f

    mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well.

    # We can ignore the micro version of PyTorch
    pip install mmcv-full -f

    See here for different versions of MMCV compatible to different PyTorch and CUDA versions.

    Optionally you can compile mmcv from source if you need to develop both mmcv and mmfewshot. Refer to the guide for details.

  2. Install MMClassification and MMDetection.

    You can simply install mmclassification and mmdetection with the following command:

    pip install mmcls mmdet
  3. Install MMFewShot.

    You can simply install mmfewshot with the following command:

    pip install mmfewshot

    or clone the repository and then install it:

    git clone
    cd mmfewshot
    pip install -r requirements/build.txt
    pip install -v -e .  # or "python develop"


a. When specifying -e or develop, MMFewShot is installed on dev mode , any local modifications made to the code will take effect without reinstallation.

b. If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.

c. Some dependencies are optional. Simply running pip install -v -e . will only install the minimum runtime requirements. To use optional dependencies like albumentations and imagecorruptions either install them manually with pip install -r requirements/optional.txt or specify desired extras when calling pip (e.g. pip install -v -e .[optional]). Valid keys for the extras field are: all, tests, build, and optional.

Another option: Docker Image

We provide a Dockerfile to build an image. Ensure that you are using docker version >=19.03.

# build an image with PyTorch 1.6, CUDA 10.1
docker build -t mmfewshot docker/

Run it with

docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmfewshot/data mmfewshot


To verify whether MMFewShot is installed correctly, we can run the demo code and inference a demo image.

Please refer to few shot classification demo or few shot detection demo for more details. The demo code is supposed to run successfully upon you finish the installation.

Dataset Preparation

Please refer to data preparation for dataset preparation.

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