Figure 5:Type #2of data augmentation consists of on-the-fly image batch manipulations. This is the most common form of data augmentation with Keras. The second type of data augmentation is calledin-place data augmentationoron-the-fly data augmentation. This type of data augmentation is what K...
Jitter CAM Jitter-CAM: Improving the Spatial Resolution of CAM-Based Explanations BMVC 2021 PyTorch Interpreting last layer dentifying Class Specific Filters with L1 Norm Frequency Histograms in Deep CNNs Arxiv FCP Forward Composition Propagation for Explainable Neural Reasoning Arxiv Protopool Inter...
Now that we have a saved keras.model we can modify the samepredict()function we wrote in the last blog post to predict the class of a local image file or any file via a web URL. python predict.py --image dog.001.jpg --model dc.model python predict.py --image_url https://goo.g...
* 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...
(applying elastic deformations to the images to introduce distortions, making the model more tolerant to deformations in the input data), color jittering (randomly change the hue, saturation and brightness of the images to introduce variations in color), random cropping (a portion of the image, ...