= 3.7.6 PyTorch >= 1.4 torchvision >= 0.5.0 faiss-gpu >= 1.6.1 In" /> = 3.7.6 PyTorch >= 1.4 torchvision >= 0.5.0 faiss-gpu >= 1.6.1 In" /> = 3.7.6 PyTorch >= 1.4 torchvision >= 0.5.0 faiss-gpu >= 1.6.1 In"/>

Official code for "Mean Shift for Self-Supervised Learning"

Related tags

Deep Learning machine-learning computer-vision convolutional-neural-networks representation-learning knn mean-shift self-supervised-learning
Overview

MSF

Official code for "Mean Shift for Self-Supervised Learning"

Requirements

  • Python >= 3.7.6
  • PyTorch >= 1.4
  • torchvision >= 0.5.0
  • faiss-gpu >= 1.6.1

Install PyTorch and ImageNet dataset following the official PyTorch ImageNet training code. We used Python 3.7 for our experiments.

To run NN and Cluster Alignment, you require to install FAISS.

FAISS:

Training

Following command can be used to train the MSF

python train_msf.py \
  --cos \
  --weak_strong \
  --learning_rate 0.05 \
  --epochs 200 \
  --arch resnet50 \
  --topk 10 \
  --momentum 0.99 \
  --mem_bank_size 128000 \
  --checkpoint_path <CHECKPOINT PATH> \
  <DATASET PATH>

License

This project is under the MIT license.

Comments
  • MSE calculation

    MSE calculation

    Hi,

    Thank you for sharing the code. I have a silly question regarding the MSE calculation. I found in your code, you used dist_t = 2 - 2 * torch.einsum('bc,kc->bk', [current_target, targets]). In my understanding, torch.einsum('bc,kc->bk', [current_target, targets]) is calculating elementwise multiplication and sum them in c dimension. How does this result in MSE?

    Thank you.

    opened by YunYunY 3
  • Training Time

    Training Time

    Hi,

    Nice work! Could you please provide the following details: a) How long did it take to finish the training (Resnet50 with 200 epochs)? b) How many gpus did you use? c) What gpus (memory) did you use?

    Thanks in advance!

    opened by dmlpt 2
  • run in my data(10 class)

    run in my data(10 class)

    I find a promeble in run python eval_knn.py and python eval_linear.py, the eval_linear.py is very low, but the python eval_knn.py result can get 0.95. this is why? I run in my data (10 class)

    opened by libingDY 1
  • reproducing the tsne experiments

    reproducing the tsne experiments

    hi

    I am trying to reproduce the tsne experiments that provided in the paper and do the visualization, but I am not able to get as clean results as is provided. would it be possible to share the code for that ?

    opened by seyeeet 0
  • Experiments on cifar

    Experiments on cifar

    Thanks for your great work, quite interesting. I wonder have you ever tried to experiment with small-scale datasets? Imagenet it rather big to try as you know.

    Best.

    opened by MrChenFeng 2
Owner
UMBC Vision
The Computer Vision Lab at the University of Maryland, Baltimore County (UMBC)
UMBC Vision
GitHub https://umbcvision.github.io/MSF
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