# 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: ```python import timm model = timm.create_model('tf_efficientnet_b0_ns', pretrained=True) model.eval() ``` To load and preprocess the image: ```python 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: ```python 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: ```python # 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](https://rwightman.github.io/pytorch-image-models/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). ```python 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](https://rwightman.github.io/pytorch-image-models/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% -->