Underwhelimg results when testing with personal images (+ bug fix)

[lrizzello]

Hello,

First of all, thank you for sharing your code. I ran into a few problems when trying to run your eval.py code, it would seem like the value of config.gdw_size under the condition if (config.net_size == "s"): should be 1024 instead of 512, without which the model will crash when loaded.

That being fixed, I tried running the model on some of my images and I'm running into some pretty underwhelming results. I haven't tried reproducing your results with existing datasets, but I have no doubt your reported metrics are accurate, so I must be missing a step on my end. This is what I have tried so far

import logging
import os
import sys

import torch

import backbones.mixnetm as mx
from utils.utils_callbacks import CallBackVerification
from utils.utils_logging import init_logging
import numpy as np
from scipy.spatial.distance import cdist

sys.path.append('/root/xy/work_dir/xyface/')
from config import config as cfg
from cv2 import imread, resize


if __name__ == "__main__":
    img1 = imread("o1.jpg")
    img2 = imread("o2.jpg")
    img1 = resize(img1, (112, 112)) / 255
    img2 = resize(img2, (112, 112)) / 255
    img1 = (img1 - 0.5) / 0.5
    img2 = (img2 - 0.5) / 0.5
    backbone = mx.mixnet_s(embedding_size=cfg.embedding_size, width_scale=cfg.scale, gdw_size=cfg.gdw_size).to("cpu")
    backbone.load_state_dict(torch.load(os.path.join('147836backbone.pth'), map_location=torch.device('cpu')))
    model = torch.nn.DataParallel(backbone)
    model.eval()
    img1_embeddings = model(
        torch.tensor(np.expand_dims(np.moveaxis(img1, -1, 0), axis=0)).type(torch.float)).detach().cpu().numpy()
    img2_embeddings = model(
        torch.tensor(np.expand_dims(np.moveaxis(img2, -1, 0), axis=0)).type(torch.float)).detach().cpu().numpy()
    cos_sim = 1 - cdist(img1_embeddings, img2_embeddings, 'cosine')

o1.jpg and o2.jpg are faces detected and aligned by MTCNN, as you did in your paper. As you can see, I reduce the RGB values to the [0,1] range and normalize them with mean and std 0.5, as I saw you did elsewhere in your code. I am not sure what is missing, could you provide me with some assistance, please?

You can find an example of two faces below, I get the cos_sim 0.92102719 when comparing them, which seems too high. https://drive.google.com/drive/folders/1YUY3ZkxPQFemWzQ8byHdNFUidxw5OOKp?usp=sharing

[hillaric]

I meet the same question, do you have a solution?

[lrizzello]

not yet, unfortunaly

[fdbtrs]

You need to align the testing data according to the https://github.com/fdbtrs/mixfacenets/blob/main/utils/align_trans.py

[hillaric]

Can you show the result for two images, I also align the testing data, but nothing change. You should have a try, which is more effective than your word.

[xiakj]

similar bug. mark

[sellaziz]

Did you manage to fix the issue ?

I tried different weights (from the linked dropbox) and got different results(~0.5217 with 295672backbone.pth ), which makes me wondering which are the backbone weights that allows to get the same results as the author.

Thank you for your help

[davidmcarreira]

Did you manage to fix the issue ?

I tried different weights (from the linked dropbox) and got different results(~0.5217 with 295672backbone.pth ), which makes me wondering which are the backbone weights that allows to get the same results as the author.

Thank you for your help

I also tried to replicate the results for the LFW evaluation and so far, the best results if ~0.58... Did you fixed the issue?