By default and similar to ViT [15] we train DeiT models at resolution 224×224 and we fine-tune at resolution 384×384. It relies on a distillation token ensuring that the student learns from the teacher through attention. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 84.4% accuracy) and when transferring to other tasks. Table 6 reports the numerical results in more details and additional evaluations on ImageNet V2 and ImageNet Real, that have a test set distinct from the ImageNet validation, which reduces overfitting on the validation set. They mainly come from DeiT’s better training strategy for visual transformers, at both the initial training and the fine-tuning stage. Table 8 compares DeiT transfer learning results to those of ViT [15] and state of the art convolutional architectures [45]. We’re training computer vision models that leverage Transformers, a breakthrough deep neural network architecture. Both tokens interact in the transformer through attention. It is also more correlated to the convnets prediction. We build upon the visual transformer architecture from Dosovitskiy et al. https://github.com/facebookresearch/deit. Training data-efficient image transformers & distillation through attention. This is because large matrix multiplications offer more opportunity for hardware optimization than small convolutions. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evalua- tion) on ImageNet with no external data. They obtain competitive tradeoffs in terms of speed / precision: For details see Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles and Hervé … Implementations. Song, A. Shepard, H. Adam, P. Perona, and S. J. Belongie, H. Hu, J. Gu, Z. Zhang, J. Dai, and Y. Wei, G. Huang, Y. Let Zt be the logits of the teacher model, Zs the logits of the student model. Last, when using our distillation procedure, we identify it with an alembic sign as DeiT. In this section, we briefly recall preliminaries associated with the visual transformer [15, 49], and further discuss positional encoding and resolution. The positional information is incorporated as fixed [49] or trainable [16] positional embeddings. On ImageNet Real and V2, EfficientNet-B4 has about the same speed as DeiT, and their accuracies are on par. Interestingly, the distilled model outperforms its teacher in terms of the trade-off between accuracy and throughput. We train it on a single computer in less than 3 days. • Do imagenet classifiers generalize to imagenet? Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, J. Krause, M. Stark, J. Deng, and L. Fei-Fei, 3D object representations for fine-grained categorization, 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Learning multiple layers of features from tiny images, L. H. Li, M. Yatskar, D. Yin, C. Hsieh, and K. Chang, VisualBERT: a simple and performant baseline for vision and language, F. Locatello, D. Weissenborn, T. Unterthiner, A. Mahendran, G. Heigold, J. Uszkoreit, A. Dosovitskiy, and T. Kipf, Object-centric learning with slot attention, Fixing weight decay regularization in adam, ViLBERT: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks, Automated flower classification over a large number of classes, Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, I. Radosavovic, R. P. Kosaraju, R. B. Girshick, K. He, and P. Dollár, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. Q=XWQ, K=XWK, V=XWV, using linear transformations WQ,WK,WV with the constraint k=N, meaning that the attention is in between all the input vectors. The attention mechanism is based on a trainable associative memory with (key, value) vector pairs. We add a new token, the distillation token, to the initial embeddings (patches and class token). If the cat is no longer on the crop of the data augmentation it implicitly changes the label of the image. This difference in performance is probably due to the fact that it benefits more from the inductive bias of convnets. Convolutional neural networks have been the main design paradigm for image understanding tasks, as initially demonstrated on image classification tasks. If not specified, DeiT refers to our referent model DeiT-B, which has the same architecture as ViT-B. For example, it may be useful to induce biases due to convolutions in a transformer model by using a convolutional model as teacher. In this section we assume we have access to a strong image classifier as a teacher model. Convolutional neural networks have optimized, both in terms of architecture and optimization during almost a decade, including through extensive architecture search that is prone to overfiting, as it is the case for instance for EfficientNets [48]. When we fine-tune DeiT at a larger resolution, we append the resulting operating resolution at the end, e.g, DeiT-B↑384. on iNaturalist 2018. The correlation between the class token and the distillation tokens slightly increases with the fine-tuning, which may reflect a loss of the specificity of each token. Even if we initialize them randomly and independently, during training they converge towards the same vector (cos=0.999), and the output embedding are also quasi-identical. Sun, T. He, J. Muller, R. Manmatha, M. Li, and A. Smola, H. Zhang, M. Cissé, Y. N. Dauphin, and D. Lopez-Paz, Mixup: beyond empirical risk minimization, Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang, Replicate, a lightweight version control system for machine learning, https://github.com/rwightman/pytorch-image-models. Thanks to Ari Morcos, Mark Tygert, Gabriel Synnaeve, and others colleagues at Facebook for brainstorming this axis. Facebook AI has developed a new technique called Data-efficient image Transformers (DeiT) to train computer vision models that leverage Transformers to unlock dramatic advances across many areas of Artificial Intelligence. DeiT-S and DeiT-Ti are trained in less than 3 days on 4 GPU. In principle any classical image scaling technique, like bilinear interpolation, could be used. The evolution of the state of the art on the ImageNet dataset [39] reflects the progress with convolutional neural network architectures and learning [29, 41, 45, 47, 48, 54]. We also use repeated augmentation [4, 23], which provides a significant boost in performance. 12/24/2020. If you find a rendering bug, file an issue on GitHub. For everything else, email us at [email protected]. In summary, our work makes the following contributions: We show that our neural networks that contains no convolutional layer can achieve competitive results against the state of the art on ImageNet with no external data. Where did the Transformer pay attention to in this image? We observe that increasing the number of epochs significantly improves the performance of training with distillation. This class token is inherited from NLP [14], and departs from the typical pooling layers used in computer vision to predict the class. (read more), Ranked #2 on Where did the Transformer pay attention to in this image? [15] is an architecture directly inherited from Natural Language Processing [49], but applied to image classification with raw image patches as input. These h sequences are rearranged into a N×dh sequence that is reprojected by a linear layer into N×D. 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