During this process, we kept increasing the size of the student model to improve the performance. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. Yalniz et al. Copyright and all rights therein are retained by authors or by other copyright holders. ImageNet . We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. We then perform data filtering and balancing on this corpus. - : self-training_with_noisy_student_improves_imagenet_classification Noisy StudentImageNetEfficientNet-L2state-of-the-art. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. The inputs to the algorithm are both labeled and unlabeled images. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet Astrophysical Observatory. We used the version from [47], which filtered the validation set of ImageNet. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. We also study the effects of using different amounts of unlabeled data. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Noisy Students performance improves with more unlabeled data. Noisy Student can still improve the accuracy to 1.6%. Ranked #14 on We present a simple self-training method that achieves 87.4 Self-training with Noisy Student improves ImageNet classification Abstract. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Self-Training With Noisy Student Improves ImageNet Classification. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Zoph et al. to use Codespaces. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. To achieve strong results on ImageNet, the student model also needs to be large, typically larger than common vision models, so that it can leverage a large number of unlabeled images. Train a larger classifier on the combined set, adding noise (noisy student). However, manually annotating organs from CT scans is time . First, we run an EfficientNet-B0 trained on ImageNet[69]. Our procedure went as follows. Please We duplicate images in classes where there are not enough images. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. For unlabeled images, we set the batch size to be three times the batch size of labeled images for large models, including EfficientNet-B7, L0, L1 and L2. IEEE Trans. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. A number of studies, e.g. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. Please refer to [24] for details about mFR and AlexNets flip probability. augmentation, dropout, stochastic depth to the student so that the noised Soft pseudo labels lead to better performance for low confidence data. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. We use the standard augmentation instead of RandAugment in this experiment. We improved it by adding noise to the student to learn beyond the teachers knowledge. self-mentoring outperforms data augmentation and self training. We do not tune these hyperparameters extensively since our method is highly robust to them. Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. Finally, frameworks in semi-supervised learning also include graph-based methods [84, 73, 77, 33], methods that make use of latent variables as target variables [32, 42, 78] and methods based on low-density separation[21, 58, 15], which might provide complementary benefits to our method. Compared to consistency training[45, 5, 74], the self-training / teacher-student framework is better suited for ImageNet because we can train a good teacher on ImageNet using label data. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. We then use the teacher model to generate pseudo labels on unlabeled images. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. Hence we use soft pseudo labels for our experiments unless otherwise specified. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 10687-10698 Abstract This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Noisy Student Training is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. Noisy Student leads to significant improvements across all model sizes for EfficientNet. The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. If nothing happens, download Xcode and try again. On robustness test sets, it improves ImageNet-A top . supervised model from 97.9% accuracy to 98.6% accuracy. Parthasarathi et al. It is expensive and must be done with great care. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. In this section, we study the importance of noise and the effect of several noise methods used in our model. Papers With Code is a free resource with all data licensed under. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. The performance drops when we further reduce it. For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. Their main goal is to find a small and fast model for deployment. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). In terms of methodology, For labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. It is experimentally validated that, for a target test resolution, using a lower train resolution offers better classification at test time, and a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ is proposed. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. to noise the student. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. During the generation of the pseudo First, a teacher model is trained in a supervised fashion. The comparison is shown in Table 9. We iterate this process by putting back the student as the teacher. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. ImageNet images and use it as a teacher to generate pseudo labels on 300M Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet.