Example #28Source File: transforms.py From ACAN with MIT License 5 votes def __call__(self, img): """ Args: img (numpy.ndarray (H x W x C)): Input image. Returns: img (numpy.ndarray (H x W x C)): Color jittered image. """ if not(_is_numpy_image(img)): raise ...
Fix Python 3.7 compat, will be dropping support for it soon Other misc fixes Release 0.9.12 Nov 20, 2023 Added significant flexibility for Hugging Face Hub based timm models via model_args config entry. model_args will be passed as kwargs through to models on creation. See example at htt...
Data Augmentation: A variety of data augmentation techniques to improve model robustness, such as CutOut, Color-Jitter, and Copy-Paste etc. Regularization: Techniques to prevent overfitting and improve model generalization, including Label Smoothing, OHEM, Focal Loss, and Mixup. ...
* saturation: How much to jitter saturation 0-1 0-1 0-1 RandomErasing dict config float float float str None 0.5 0.02 0.4 const The RandomErasing augmentation contains the following parameters: * erase_prob: The probability that image will be randomly erased * min_area_ratio: The mini...
Fix Python 3.7 compat, will be dropping support for it soon Other misc fixes Release 0.9.12 Nov 20, 2023 Added significant flexibility for Hugging Face Hub based timm models via model_args config entry. model_args will be passed as kwargs through to models on creation. See example at htt...
features = { transforms = ( { type = "Crop" ; cropType = "RandomSide" ; sideRatio = 0.8 ; jitterType = "UniRatio" } : { type = "Scale" ; width = 32 ; height = 32 ; channels = 3 ; interpolations = "linear" } : { type = "Transpose" } )} labels = { labelDim = 10...
To improve classification results using the inverted StyleGAN, a “jitter” technique was used to emphasize differences in fine features between classes; we refer to this technique as StyleGAN-IJ. Inspired by the sampling process of VAEs (Goodfellow et al., 2016), we introduce some Gaussian nois...
Here is an example of a linear expression. If we set ψy (w, k) = w × y + k, a mathematical model of monotonic increase or monotonic decrease, the image result will have a gradient effect on the y-axis. That is, the magnitude of the image will change from dark to light or ...
A simple data augmentation example Figure 2:Left:A sample of 250 data points that follow a normal distribution exactly.Right:Adding a small amount of random “jitter” to the distribution. This type of data augmentation increases the generalizability of our networks. ...
features = { transforms = ( { type = "Crop" ; cropType = "RandomSide" ; sideRatio = 0.8 ; jitterType = "UniRatio" } : { type = "Scale" ; width = 32 ; height = 32 ; channels = 3 ; interpolations = "linear" } : { type = "Transpose" } )} labels = { labelDim = 10...