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import os
import random
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from scipy.ndimage import shift as scipy_shift
import json

DATA_PATH = 'data'
OUTPUT_PATH = 'dataset'

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import React, { useEffect, useRef } from "react";

const Oneko = () => {
  const nekoElRef = useRef(null);
  const timeoutIdRef = useRef(null);

  useEffect(() => {
    const isReduced =
      window.matchMedia(`(prefers-reduced-motion: reduce)`) === true ||
      window.matchMedia(`(prefers-reduced-motion: reduce)`).matches === true;

alxfgh

# PyTorch implementation of "Half-UNet: A Simplified U-Net Architecture for Medical Image Segmentation"
import torch
import torch.nn as nn
import torch.nn.functional as F

class GhostModule(nn.Module):
    def __init__(self, in_channels, out_channels=64):
        super(GhostModule, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels // 2, 3, padding=1, bias=False)
        self.batch_norm = nn.BatchNorm2d(out_channels // 2)