# Noisy Student (EfficientNet)

**Noisy Student Training** is a semi-supervised learning approach. It extends the idea of self-training
and distillation with the use of equal-or-larger student models and noise added to the student during learning. It has three main steps: 

1. train a teacher model on labeled images
2. use the teacher to generate pseudo labels on unlabeled images
3. train a student model on the combination of labeled images and pseudo labeled images. 

The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student.

Noisy Student Training seeks to improve on self-training and distillation in two ways. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Second, it adds noise to the student so the noised student is forced to learn harder from the pseudo labels. To noise the student, it uses input noise such as RandAugment data augmentation, and model noise such as dropout and stochastic depth during training.

## How do I use this model on an image?

To load a pretrained model:

```py
>>> import timm
>>> model = timm.create_model('tf_efficientnet_b0_ns', pretrained=True)
>>> model.eval()
```

To load and preprocess the image:

```py 
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform

>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)

>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```

To get the model predictions:

```py
>>> import torch
>>> with torch.no_grad():
...     out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```

To get the top-5 predictions class names:

```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename) 
>>> with open("imagenet_classes.txt", "r") as f:
...     categories = [s.strip() for s in f.readlines()]

>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
...     print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```

Replace the model name with the variant you want to use, e.g. `tf_efficientnet_b0_ns`. You can find the IDs in the model summaries at the top of this page.

To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.

## How do I finetune this model?

You can finetune any of the pre-trained models just by changing the classifier (the last layer).

```py
>>> model = timm.create_model('tf_efficientnet_b0_ns', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.

## How do I train this model?

You can follow the [timm recipe scripts](../scripts) for training a new model afresh.

## Citation

```BibTeX
@misc{xie2020selftraining,
      title={Self-training with Noisy Student improves ImageNet classification}, 
      author={Qizhe Xie and Minh-Thang Luong and Eduard Hovy and Quoc V. Le},
      year={2020},
      eprint={1911.04252},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```

<!--
Type: model-index
Collections:
- Name: Noisy Student
  Paper:
    Title: Self-training with Noisy Student improves ImageNet classification
    URL: https://paperswithcode.com/paper/self-training-with-noisy-student-improves
Models:
- Name: tf_efficientnet_b0_ns
  In Collection: Noisy Student
  Metadata:
    FLOPs: 488688572
    Parameters: 5290000
    File Size: 21386709
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    Tasks:
    - Image Classification
    Training Techniques:
    - AutoAugment
    - FixRes
    - Label Smoothing
    - Noisy Student
    - RMSProp
    - RandAugment
    - Weight Decay
    Training Data:
    - ImageNet
    - JFT-300M
    Training Resources: Cloud TPU v3 Pod
    ID: tf_efficientnet_b0_ns
    LR: 0.128
    Epochs: 700
    Dropout: 0.5
    Crop Pct: '0.875'
    Momentum: 0.9
    Batch Size: 2048
    Image Size: '224'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
    Stochastic Depth Survival: 0.8
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1427
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 78.66%
      Top 5 Accuracy: 94.37%
- Name: tf_efficientnet_b1_ns
  In Collection: Noisy Student
  Metadata:
    FLOPs: 883633200
    Parameters: 7790000
    File Size: 31516408
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    Tasks:
    - Image Classification
    Training Techniques:
    - AutoAugment
    - FixRes
    - Label Smoothing
    - Noisy Student
    - RMSProp
    - RandAugment
    - Weight Decay
    Training Data:
    - ImageNet
    - JFT-300M
    Training Resources: Cloud TPU v3 Pod
    ID: tf_efficientnet_b1_ns
    LR: 0.128
    Epochs: 700
    Dropout: 0.5
    Crop Pct: '0.882'
    Momentum: 0.9
    Batch Size: 2048
    Image Size: '240'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
    Stochastic Depth Survival: 0.8
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1437
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ns-99dd0c41.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 81.39%
      Top 5 Accuracy: 95.74%
- Name: tf_efficientnet_b2_ns
  In Collection: Noisy Student
  Metadata:
    FLOPs: 1234321170
    Parameters: 9110000
    File Size: 36801803
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    Tasks:
    - Image Classification
    Training Techniques:
    - AutoAugment
    - FixRes
    - Label Smoothing
    - Noisy Student
    - RMSProp
    - RandAugment
    - Weight Decay
    Training Data:
    - ImageNet
    - JFT-300M
    Training Resources: Cloud TPU v3 Pod
    ID: tf_efficientnet_b2_ns
    LR: 0.128
    Epochs: 700
    Dropout: 0.5
    Crop Pct: '0.89'
    Momentum: 0.9
    Batch Size: 2048
    Image Size: '260'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
    Stochastic Depth Survival: 0.8
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1447
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ns-00306e48.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 82.39%
      Top 5 Accuracy: 96.24%
- Name: tf_efficientnet_b3_ns
  In Collection: Noisy Student
  Metadata:
    FLOPs: 2275247568
    Parameters: 12230000
    File Size: 49385734
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    Tasks:
    - Image Classification
    Training Techniques:
    - AutoAugment
    - FixRes
    - Label Smoothing
    - Noisy Student
    - RMSProp
    - RandAugment
    - Weight Decay
    Training Data:
    - ImageNet
    - JFT-300M
    Training Resources: Cloud TPU v3 Pod
    ID: tf_efficientnet_b3_ns
    LR: 0.128
    Epochs: 700
    Dropout: 0.5
    Crop Pct: '0.904'
    Momentum: 0.9
    Batch Size: 2048
    Image Size: '300'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
    Stochastic Depth Survival: 0.8
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1457
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 84.04%
      Top 5 Accuracy: 96.91%
- Name: tf_efficientnet_b4_ns
  In Collection: Noisy Student
  Metadata:
    FLOPs: 5749638672
    Parameters: 19340000
    File Size: 77995057
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    Tasks:
    - Image Classification
    Training Techniques:
    - AutoAugment
    - FixRes
    - Label Smoothing
    - Noisy Student
    - RMSProp
    - RandAugment
    - Weight Decay
    Training Data:
    - ImageNet
    - JFT-300M
    Training Resources: Cloud TPU v3 Pod
    ID: tf_efficientnet_b4_ns
    LR: 0.128
    Epochs: 700
    Dropout: 0.5
    Crop Pct: '0.922'
    Momentum: 0.9
    Batch Size: 2048
    Image Size: '380'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
    Stochastic Depth Survival: 0.8
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1467
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ns-d6313a46.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 85.15%
      Top 5 Accuracy: 97.47%
- Name: tf_efficientnet_b5_ns
  In Collection: Noisy Student
  Metadata:
    FLOPs: 13176501888
    Parameters: 30390000
    File Size: 122404944
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    Tasks:
    - Image Classification
    Training Techniques:
    - AutoAugment
    - FixRes
    - Label Smoothing
    - Noisy Student
    - RMSProp
    - RandAugment
    - Weight Decay
    Training Data:
    - ImageNet
    - JFT-300M
    Training Resources: Cloud TPU v3 Pod
    ID: tf_efficientnet_b5_ns
    LR: 0.128
    Epochs: 350
    Dropout: 0.5
    Crop Pct: '0.934'
    Momentum: 0.9
    Batch Size: 2048
    Image Size: '456'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
    Stochastic Depth Survival: 0.8
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1477
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ns-6f26d0cf.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 86.08%
      Top 5 Accuracy: 97.75%
- Name: tf_efficientnet_b6_ns
  In Collection: Noisy Student
  Metadata:
    FLOPs: 24180518488
    Parameters: 43040000
    File Size: 173239537
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    Tasks:
    - Image Classification
    Training Techniques:
    - AutoAugment
    - FixRes
    - Label Smoothing
    - Noisy Student
    - RMSProp
    - RandAugment
    - Weight Decay
    Training Data:
    - ImageNet
    - JFT-300M
    Training Resources: Cloud TPU v3 Pod
    ID: tf_efficientnet_b6_ns
    LR: 0.128
    Epochs: 350
    Dropout: 0.5
    Crop Pct: '0.942'
    Momentum: 0.9
    Batch Size: 2048
    Image Size: '528'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
    Stochastic Depth Survival: 0.8
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1487
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ns-51548356.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 86.45%
      Top 5 Accuracy: 97.88%
- Name: tf_efficientnet_b7_ns
  In Collection: Noisy Student
  Metadata:
    FLOPs: 48205304880
    Parameters: 66349999
    File Size: 266853140
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    Tasks:
    - Image Classification
    Training Techniques:
    - AutoAugment
    - FixRes
    - Label Smoothing
    - Noisy Student
    - RMSProp
    - RandAugment
    - Weight Decay
    Training Data:
    - ImageNet
    - JFT-300M
    Training Resources: Cloud TPU v3 Pod
    ID: tf_efficientnet_b7_ns
    LR: 0.128
    Epochs: 350
    Dropout: 0.5
    Crop Pct: '0.949'
    Momentum: 0.9
    Batch Size: 2048
    Image Size: '600'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
    Stochastic Depth Survival: 0.8
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1498
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 86.83%
      Top 5 Accuracy: 98.08%
- Name: tf_efficientnet_l2_ns
  In Collection: Noisy Student
  Metadata:
    FLOPs: 611646113804
    Parameters: 480310000
    File Size: 1925950424
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    Tasks:
    - Image Classification
    Training Techniques:
    - AutoAugment
    - FixRes
    - Label Smoothing
    - Noisy Student
    - RMSProp
    - RandAugment
    - Weight Decay
    Training Data:
    - ImageNet
    - JFT-300M
    Training Resources: Cloud TPU v3 Pod
    Training Time: 6 days
    ID: tf_efficientnet_l2_ns
    LR: 0.128
    Epochs: 350
    Dropout: 0.5
    Crop Pct: '0.96'
    Momentum: 0.9
    Batch Size: 2048
    Image Size: '800'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
    Stochastic Depth Survival: 0.8
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1520
  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth
  Results:
  - Task: Image Classification
    Dataset: ImageNet
    Metrics:
      Top 1 Accuracy: 88.35%
      Top 5 Accuracy: 98.66%
-->