This is an PyTorch implementation of MNN proposed by our paper MNN: Mixed Nearest-Neighbors for Self-Supervised Learning. If you find this repo useful, welcome 🌟🌟🌟✨.

Step 0. Download and install Miniconda from official website

Step 1. Create a conda environment and activate it

conda create --name mnn python=3.9 -y
conda activate mnn

Step 2. Install PyTorch following official instructions, e.g.

pip install -r requirements.txt

Step 3. Install MNN

git clone https://github.com/pc-cp/MNN
cd MNN
chmod +x ./scripts.sh
./scripts.sh

• CIFAR10/CIFAR100: https://www.cs.toronto.edu/~kriz/cifar.html
• STL10: http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz
• Tiny ImageNet: http://cs231n.stanford.edu/tiny-imagenet-200.zip

• Ubuntu 20.04.4 LTS (Focal Fossa)
• Python 3.9.7

You can download pretrained models here:

• this link trained on three datasets.
• Download and place in the "./checkpoints" directory.

Our model achieves the following performance:

📋 If there are any questions, feel free to contact with the authors.