( Image credit: Albumentations )
In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.
Ranked #3 on Image Classification on SVHN
On LibriSpeech, we achieve 6. 8% WER on test-other without the use of a language model, and 5. 8% WER with shallow fusion with a language model.
Ranked #1 on Speech Recognition on Hub5'00 SwitchBoard
Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models.
Ranked #80 on Image Classification on ImageNet
Contrastive self-supervised learning (CSL) is an approach to learn useful representations by solving a pretext task that selects and compares anchor, negative and positive (APN) features from an unlabeled dataset.
Ranked #20 on Image Classification on STL-10
By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87. 3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2. 0% accuracy while training 5x-11x faster using the same computing resources.
Ranked #2 on Image Classification on Stanford Cars
Additionally, due to the separate search phase, these approaches are unable to adjust the regularization strength based on model or dataset size.
Ranked #2 on Image Classification on SVHN
In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).
Ranked #3 on Image Classification on Fashion-MNIST
We provide examples of image augmentations for different computer vision tasks and show that Albumentations is faster than other commonly used image augmentation tools on the most of commonly used image transformations.