Models can be trained with RandAugment for the dataset of interest with no need for a separate proxy task. By only tuning two hyperparameters(N, M), you can achieve competitive performances as AutoAugments. Ins
Models can be trained with RandAugment for the dataset of interest with no need for a separate proxy task. By only tuning two hyperparameters(N, M), you can achieve competitive performances as AutoAugments. Install $ pip install git+https://github.com/ildoonet/pytorch-randaugment ...
Unofficial PyTorch Reimplementation of RandAugment. - pytorch-randaugment/setup.py at master · mil-tokyo/pytorch-randaugment
If I return aug only instead of aug.transforms.insert(0, RandAugment(4, 3)), there is no error. Error TypeError Traceback (most recent call last) <timed exec> in <module> D:\Datasets\Image dataset\Xray\SIAMESE-classifier\src\cross_vals.py in kfoldcv(model, data, epochs, n_splits,...
pytorch-randaugment Unofficial PyTorch Reimplementation of AutoAugment and RandAugment. Code taken fromhttps://github.com/DeepVoltaire/AutoAugmentandhttps://github.com/jizongFox/uda How to install: pip install randaugment How to use: fromrandaugmentimportRandAugment,ImageNetPolicydata=ImageFolder(rootdir,tran...