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

Billion-scale semi-supervised learning for image classification paper
Author: I. Zeki Yalniz, Herve J ´ egou, Kan Chen, Manohar Paluri, Dhruv Mahajan (Facebook AI)

Network Architecture

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.

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
```

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 !!