bad result from lfw dataset

hello @peteryuX i try to test accuracy on 2 images of 1 person in lfw dataset. The distance is big.

my test code:

from absl import app, flags, logging
from absl.flags import FLAGS
import cv2
import os
import numpy as np
import tensorflow as tf

from modules.evaluations import get_val_data, perform_val
from modules.models import ArcFaceModel
from modules.utils import set_memory_growth, load_yaml, l2_norm
from scipy.spatial.distance import cosine


flags.DEFINE_string('cfg_path', './configs/arc_res50.yaml', 'config file path')
flags.DEFINE_string('gpu', '0', 'which gpu to use')
flags.DEFINE_string('img_path', '', 'path to input image')


def main(_argv):
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
    os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu

    logger = tf.get_logger()
    logger.disabled = True
    logger.setLevel(logging.FATAL)
    set_memory_growth()

    cfg = load_yaml(FLAGS.cfg_path)

    model = ArcFaceModel(size=cfg['input_size'],
                         backbone_type=cfg['backbone_type'],
                         training=False)

    ckpt_path = tf.train.latest_checkpoint('./checkpoints/')
    if ckpt_path is not None:
        print("[*] load ckpt from {}".format(ckpt_path))
        model.load_weights(ckpt_path)
    else:
        print("[*] Cannot find ckpt from {}.".format(ckpt_path))
        exit()
    image_fol = "./tmp"
    paths = os.listdir(image_fol)
    embeds = []
    images = []
    flip_images = []
    for path in paths:
        print(path)
        img = cv2.imread(os.path.join(image_fol, path))
        img = cv2.resize(img, (cfg['input_size'], cfg['input_size']))
        img = img.astype(np.float32) / 255.
        # if len(img.shape) == 3:
        #     img = np.expand_dims(img, 0)
        # embeds.append(l2_norm(model(img)))
        images.append(img)
    images = np.array(images)
    def hflip_batch(imgs):
        assert len(imgs.shape) == 4
        return imgs[:, :, ::-1, :]

    flip_images = hflip_batch(images)
    embeds = model(images) + model(flip_images)
    embeds = l2_norm(embeds)

    dist = np.sum(np.square(embeds[0]-embeds[1]))

    print("dist: ", dist)
    # diff = np.subtract([embeds[0]], [embeds[1]])
    # dist = np.sum(np.square(diff), 1)
    # print("diff: ", diff)
    acc = 1 - cosine(embeds[0], embeds[1])
    print("acc: ", acc)

the result is:

dist:  1.1708782
acc:  0.4145609736442566

How can i achive 99.35% accuracy on lfw?

[HoangTienDuc]

hello @FelixZhang7 It is exactly similarity, not acc.

[FelixZhang7]

@HoangTienDuc Hello，may i ask why acc = 1 - cosine(embeds[0], embeds[1])?