Peter Lorenz

[Feature Request] Save the XML file in the same directory as the image file is found.

5

- **OS:** Windows - **PyQt version:** 5 I would like to have the feature, that annotation is saved automatically in the same path as the image file. My problem: I...

https://huggingface.co/nousr/conditioned-prior I would like to use the text 2 image model.

I am using this template: https://github.com/brenhinkeller/preprint-template.tex In overleaf, I get this error. This error does not hamp creating an pdf and just appears in the raw output or when uploading...

It always predicts 464 for every sample... ```python import torch import pickle as pkl import time import numpy as np import cv2 import matplotlib.pyplot as plt import torchvision.models as models...

How are the ImageNet (512x512 pixels) images created? I mean, if you train a confusion model, you to prepare the dataset in advance.

Here: https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/blob/863b758ee4f4a6d3d4e7777c5f94f457fa449f73/imagenet/convert.py#L22 This resizing is a bilinear interpolation, which can produce some loss. Nearest neighbor is said to be lossless: ``` resized_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_NEAREST) ```

Mine: ```python dataset = datasets.CIFAR10(root='./data', download=True, train=True, transform=transforms.ToTensor()) mean = dataset.data.astype(float).mean(axis=(0,1,2)) / 255 std = dataset.data.astype(float).std(axis=(0,1,2)) / 255 print("cifar10", np.round(mean, 4), np.round(std, 4)) --> cifar10 [0.4914 0.4822 0.4465] [0.247 0.2435...

Do I need to set the widen factor to 1? or 0? ```python main --lr 0.1 resume false --net_type [lenet/vggnet/resnet/wide-resnet] --depth 34 --widen_factor 1 --dropout_rate 0.3 --dataset cifar10``

There are two ways to use the API: To use the criteria: ```python raw_advs, clipped_advs, success = attack(fmodel, data, criterion=Misclassification(label), epsilons=args.eps) ``` and without using the criteria: ```python raw_advs, clipped_advs,...

``` from folder2lmdb import ImageFolderLMDB from torch.utils.data import DataLoader from torchvision.transforms import transforms import torchvision dir_folder = "/home/DATA//lsun/church/church_outdoor_train_lmdb/data.mdb" transform = transforms.Compose([ transforms.ToTensor(), ]) dataset = ImageFolderLMDB(dir_folder, transform, transform) # 33...