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Implementing Billion-scale semi-supervised learning for image classification using Pytorch

Network Architecture

캡처

  • Step 1:
    • We train on the labeled data to get an initial teacher model
  • Step 2:
    • For each class/label, we use the predictions of this teacher model to rank the unlabeled images and pick top-K images to construct a new training data
  • Step 3:
    • We use this data to train a student model, which typically differs from the teacher model: hence we can target to reduce the complexity at test time
  • Step 4:
    • finally, pre-trained student model is fine-tuned on the initial labeled data to circumvent potential labeling errors.

Usage

  • Step 1:
    • If there is a pretrained weight of the teacher network, go to step 2.
    • If you do not have pretrained weights, run the following command to train the teacher network.
    python main.py
    
  • Step 2:
    • Sampling unlabeled data through a pretrained teacher network.
    python make_sample_data.py
    
  • Step 3:
    • Students learn the student network using the data sampled in Step 2.
    python student_train.py
    
  • Step 4:
    • Finally, fine-tuning the CIFAR-100 data using the student network trained using unlabeled data in Step 3.
    python main.py --student-network True
    

Unlabeled Data

  • Image crawler

Experiments

  • In the paper, K=16k, P=10, Dataset=ImageNet, Unlabeled Data: 1,000,000,000 images.
  • However, we do not have many GPUs, so we are training at CIFAR-100.
    • Ours) K=1000, P=10, Dataset=CIFAR-100, Unlabeled Data: About 150,000 images.
Datasets Model Accuracy Epoch Training Time
CIFAR-100 ResNet-50 76.36% 91 3h 31m
CIFAR-100 ResNet-50, Semi-Supervisied learning(WORK IN PROCESS)
  • For CIFAR-100 data, the image size is too small, so the result is not good when the unlabeled data is reduced to (32, 32).
    • We will solve this problem !!

Requirements

  • tqdm==4.31.1
  • torch==1.0.1
  • opencv version: 4.1.0

Reference