Parametric Contrastive Learning (ICCV2021)

Related tags

Deep Learning pytorch supervised-learning imagenet image-classification class-imbalance imbalanced-data imbalanced-learning contrastive-learning supervised-contrastive-learning long-tailed-recognition iccv2021 parametric-contrastive-learning
Overview

Parametric-Contrastive-Learning

This repository contains the implementation code for ICCV2021 paper:
Parametric Contrastive Learning (https://arxiv.org/abs/2107.12028)

PWC

PWC

PWC

If you find this code or idea useful, please consider citing our work:

@misc{cui2021parametric,
      title={Parametric Contrastive Learning}, 
      author={Jiequan Cui and Zhisheng Zhong and Shu Liu and Bei Yu and Jiaya Jia},
      year={2021},
      eprint={2107.12028},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Overview

In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalance learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our PaCo loss under a balanced setting. Our analysis demonstrates that PaCo can adaptively enhance the intensity of pushing samples of the same class close as more samples are pulled together with their corresponding centers and benefit hard example learning. Experiments on long-tailed CIFAR, ImageNet, Places, and iNaturalist 2018 manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models trained with PaCo loss surpass supervised contrastive learning across various ResNet backbones.

Results and Pretrained models

Full ImageNet (Balanced setting)

Method Model Top-1 Acc(%) link log
PaCo ResNet-50 79.3 download download
PaCo ResNet-101 80.9 download download
PaCo ResNet-200 81.8 download download

ImageNet-LT (Imbalance setting)

Method Model Top-1 Acc(%) link log
PaCo ResNet-50 57.0 download download
PaCo ResNeXt-50 58.2 download download
PaCo ResNeXt-101 60.0 download download

iNaturalist 2018 (Imbalanced setting)

Method Model Top-1 Acc(%) link log
PaCo ResNet-50 73.2 TBD download
PaCo ResNet-152 75.2 TBD download

Places-LT (Imbalanced setting)

Method Model Top-1 Acc(%) link log
PaCo ResNet-152 41.2 TBD download

Get Started

For full ImageNet, ImageNet-LT, iNaturalist 2018, Places-LT training and evaluation. Note that PyTorch>=1.6. All experiments are conducted on 4 GPUs. If you have more GPU resources, please make sure that the learning rate should be linearly scaled and 32 images per gpu is recommented.

cd Full-ImageNet
bash sh/train_resnet50.sh
bash sh/eval_resnet50.sh

cd LT
bash sh/ImageNetLT_train_R50.sh
bash sh/ImageNetLT_eval_R50.sh
bash sh/PlacesLT_train_R152.sh
bash sh/PlacesLT_eval_R152.sh

Contact

If you have any questions, feel free to contact us through email ([email protected]) or Github issues. Enjoy!

Comments
  • Question about CIFAR-LT experiment

    Question about CIFAR-LT experiment

    Hi! thank you for your interesting work!

    I have a question on hyperparameters in CIFAR-LT dataset experiments

    The paper explains alpha and moco temperature, but there is no explanation for the rest of the hyper-parameters(beta, gamma, augmentation stradegy). Can you please provide it?

    opened by caisarl76 6
  • Questions about experiments

    Questions about experiments

    Thanks for your ICCV work. However, I find you directly use the test set of ImageNet-LT to store the best models, which may lead to overfitting in practice and seems to be unfair to other compared methods. Could you please provide the results on ImageNet-LT using the validation set to store models? It would be easier for us to compare with PaCo in our work. Thanks very much.

    opened by Hongbin98 6
  • Questions about tau-norm with randaugment

    Questions about tau-norm with randaugment

    Thanks for your exciting work! Tau-norm with randaugment performs so well as shown in Table 3 and Table 5. I wonder about its implementation, just use augmentation_randncls as train_transform in training stage-1?

    opened by adf1178 5
  • Questions about NormedLinear_Classifier

    Questions about NormedLinear_Classifier

    Thanks for your great work. I am trying to use NormedLinear_Classifier since I may modify it later. But, it is not good now. Could you please tell me the hyperparameters when your training, such as lr, supt in loss?

    opened by JingLiJJ 4
  • The meaning of beta and gamma in the code

    The meaning of beta and gamma in the code

    parser.add_argument('--beta', default=1.0, type=float, help='supervise loss weight') parser.add_argument('--gamma', default=1.0, type=float, help='paco loss') I found these two parameters in the code, and they are used in losses.py, but I can't understand the using of them, are they mentioned in the paper? If you can explain them to me, I will very appreciate on you, thank you!

    opened by madoka109 4
  • The checkpoint on iNat2018

    The checkpoint on iNat2018

    Thank you guys for your impressive work and releasing the code. I just wonder when will the checkpoint on iNat2018 be released? I'm looking forward to that. Thanks a lot!

    opened by Duconnor 3
  • FileNotFoundError: [Errno 2] No such file or directory: 'pretrained_model/moco_ckpt.best.pth.tar' raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException:

    FileNotFoundError: [Errno 2] No such file or directory: 'pretrained_model/moco_ckpt.best.pth.tar' raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException:

    Hi, Thank you so much for your work. I am facing this issue while using this repo. I don't know where to upload this file because this folder is not there.

    opened by noreenanwar 1
  • Different resnet backbones

    Different resnet backbones

    Hi thanks for the great work. I notice that you have used different renet backbones for cifar training (load from resnet_cifar) and imagenet training (load from resnet_imagenet). Is there a reason why you use different backbones?

    opened by thomascong121 1
  • Question about inference function in moco/builder.py

    Question about inference function in moco/builder.py

    Hello, I'm impressed with your work and thank you for sharing the codes.

    I have a question abut inference function in LT/moco/builder.py ! In the code below, you are not using q but using self.feat_after_avg_q as input for linear. Then, it doesn't seem to need the first two lines in the code. Or, should self.feat_after_avg_q be changed to q? Can you check this? Thank you in advance! :)

    _def _inference(self, image): q = self.encoder_q(image) q = nn.functional.normalize(q, dim=1) encoder_q_logits = self.linear(self.feat_after_avg_q)

        return encoder_q_logits_
    opened by pseulki 1
  • Question about supcon framework

    Question about supcon framework

    Hi, thanks for the great work, I have a question about the selection of the framework. Since you use the supervised contrastive loss, why not use the framework from supcon and use MoCo framework instead?

    opened by thomascong121 1
  • Questions about center learning

    Questions about center learning

    Hi, thanks for your exciting work.
    I have a question about the centre learning that I did not find any function in the paper, and I did not find any comment of centres on the code. Could you give any clue about how could we the parametric centres?

    I am looking forward to your reply.

    opened by zuglerQ 1
Owner
DV Lab
Deep Vision Lab
DV Lab
GitHub https://arxiv.org/abs/2107.12028
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