Unsupervised Representation Learning by Invariance Propagation

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Deep Learning InvariancePropagation
Overview

Unsupervised Learning by Invariance Propagation

This repository is the official implementation of Unsupervised Learning by Invariance Propagation.

Pretraining on Natual Images

Train on ImageNet

To train the model(s) in the paper, run this command:

python main.py --exp 'your_path' --n_background 4096 --t 0.2 --blur --cos --network 'resnet50' --nonlinearhead 1 --weight_decay 1e-4

Evaluation

To evaluate the model on ImageNet, run:

python -m downstream.linear_classification.linear_classification --gpus '0,1' --exp 'your_exp_path' --pretrained_path 'pretrain_path' --backbone 'resnet50'

Notice that in the paper, to calculate the BFS results, we require to record the id of neighbours of each anchor point. For computational efficiency, we apprximate the BFS results by only concatenating the neighbours of each point, up to L steps. This results may be a little different with the real BFS results due to there exists repeated samples, however it works pretty well, both effectively and efficiently. Pretrained model can be found here.

Train on Cifar

To train the model(s) in cifar10 and cifar100 or svhn, run this command:

# cifar10
python main.py --exp 'your_path' -n_background 4096 --t 0.2 --blur --cos --network 'resnet18_cifar' --nonlinearhead 1 --weight_decay 5e-4 --n_pos 20 --dataset 'cifar10'
# cifar100
python main.py --exp 'your_path' -n_background 4096 --t 0.2 --blur --cos --network 'resnet18_cifar' --nonlinearhead 1 --weight_decay 5e-4 --n_pos 20 --dataset 'cifar100'
# svhn
python main.py --exp 'your_path' -n_background 4096 --t 0.2 --blur --cos --network 'resnet18_cifar' --nonlinearhead 1 --weight_decay 5e-4 --n_pos 20 --dataset 'svhn'

Evaluation

To train the model(s) in cifar10 and cifar100 run this command:

# cifar10
python -m downstream.linear_classification.eval_linear --gpus '0,1' --exp 'your_exp_path' --pretrained_path 'pretrain_path' --backbone 'resnet18_cifar' --dataset 'cifar10'
# cifar100
python -m downstream.linear_classification.eval_linear --gpus '0,1' --exp 'your_exp_path' --pretrained_path 'pretrain_path' --backbone 'resnet18_cifar' --dataset 'cifar100'
# svhn
python -m downstream.linear_classification.eval_linear --gpus '0,1' --exp 'your_exp_path' --pretrained_path 'pretrain_path' --backbone 'resnet18_cifar' --dataset 'svhn'

Pretraining on Defect Classification Dataset

For validate the effectiveness and practicabilities of the proposed algorithms, we can also train and evaluate our method on Defect Detection Dataset.

Train on WM811.

python main.py --gpus '0,1,2' --exp 'output/' --n_background 4096 --t 0.07 --cos --network 'resnet18_wm811' --dataset 'wm811' --nonlinearhead 0 --weight_decay 5e-4

Evaluation

To evaluate the model on WM811, run:

python -m downstream.fine_tune_wm811 --save_folder 'your_output_folder' --model_path 'your_pretrain_model' --model 'resnet18_wm811' --dataset 'wm811' --weight_decay 1e-3 --learning_rate1 0.001 --learning_rate2 0.002 --label_smoothing 0.1 --dropout 0.5
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Comments
  • CIFAR config

    CIFAR config

    Hi! Thanks for your inspiring work.

    I notice there is some code about CIFAR training in this repository. Could you please specify one complete config of this part for a fast validation like 'resnet18 + cifar100'?

    Thanks a lot!

    opened by YAaNnGg 2
  • 关于hard negative samples的问题

    关于hard negative samples的问题

    作者您好，非常感谢您能公布代码。

    在您论文Hard Sampling Strategy部分提到N_neg(i) = NM(i) − N (i)，并且background set B(i) = N_neg(i) ∪ Nh(i)。但是在objective.py中background set只是减去了self_sim。

    opened by Chunwei-Wu 1
  • 报告两处小问题

    报告两处小问题

    您好，我还是一个初学者。 在复现您的工作时，使用代码中train on cifar 10的代码做了训练，并用相应的evaluate部分想测试下。 装完对应的库以后，显示的却是如下错误：argument for training: error: argument --gpu: invalid int value: '0,1' 我猜测可能是您提供的指令与代码不匹配。 另外这段指令的在readme中的描述写的是train而非evaluate。

    opened by ludysama 0
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FengWang
FengWang
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