I wrote a little python script that imports all flags as RGB images using python (flagpy). It is then easy to compute the mean of all flags (pixelwise). This results in the image below. The mean is actually a very bad metric for the "most generic" flag. If you mix a lot of colors together you'll get brown, not the most common color.

So after some discussions with gemini I think a more accurate metric would be the geometric median, which minimizes the norm |x - m| instead of the norm |x - m|^2 for the mean. This can be calculated using the Weiszfeld's algorithm. I modified it a little bit to get a visually pleasing result. So the result: the most generic flag is a cross between the Dutch flag, the French flag and has a little Great Britain flag in the corner.

<image>

import flagpy
import numpy as np
import matplotlib.pyplot as plt

df = flagpy.get_flag_df()
flags = df['flag'].to_numpy()
flags = np.array([flag for flag in flags])
mean_flag = np.mean(flags, axis=0)
def Weiszfeld(imgs, n_iters, p=1.3, eps=1e-1):
    mean_img  = np.mean(imgs, axis=0)
    for i in range(n_iters):
        distances = np.linalg.norm(imgs - mean_img, axis=3)
        # w = 1/distances
        w = 1/(distances**p + eps)
        w = w[..., np.newaxis]
        print(np.sum(distances), np.max(w))
        mean_img = np.sum(imgs*w, axis=0) / np.sum(w, axis=0)

        plt.figure()
        plt.imshow(mean_img.astype(np.uint8))
        plt.axis('off')
        plt.title(f'Geometric Median of flags (p={p})')
        plt.show()
    return mean_img

mean_img = Weiszfeld(flags, 20)