训练结果acc 为什么一直这么低，但是lfw face verification accuracy: 0.9095 threshold: 0.37753257 结果是这样的

[cqray1990]

total time is 254.24768543243408, average time is 0.9892906047954634
lfw face verification accuracy: 0.9095 threshold: 0.37753257
Sat Dec 23 08:19:27 2023 train epoch 2 iter 10 0.08301005559180279 iters/s loss 12.163952827453613 acc 0.0234375
Sat Dec 23 08:35:10 2023 train epoch 2 iter 110 0.10600735235136766 iters/s loss 11.601116180419922 acc 0.02734375
Sat Dec 23 08:51:06 2023 train epoch 2 iter 210 0.10467295813176283 iters/s loss 11.698826789855957 acc 0.02734375
Sat Dec 23 09:07:12 2023 train epoch 2 iter 310 0.10353925427018165 iters/s loss 11.612323760986328 acc 0.0390625
Sat Dec 23 09:23:40 2023 train epoch 2 iter 410 0.10121389428942351 iters/s loss 11.697545051574707 acc 0.04296875
Sat Dec 23 09:39:57 2023 train epoch 2 iter 510 0.10231825089000418 iters/s loss 11.737585067749023 acc 0.046875
Sat Dec 23 09:57:14 2023 train epoch 2 iter 610 0.09639180206759532 iters/s loss 11.6640625 acc 0.03515625

训练数据是CASIA-WebFace 10575的那份数据

以下是配置文件：
class Config(object):
env = 'default'
backbone = 'resnet18'
classify = 'softmax'
num_classes = 13938 #10575
metric = 'arc_margin'
easy_margin = False
use_se = False
loss = 'focal_loss'

display = False
finetune = False

train_root = /facedata/CASIA-WebFace_align/'
train_list = '/img_train.txt'
#val_list = '/data/Datasets/webface/val_data_13938.txt'

#test_root = '/data1/Datasets/anti-spoofing/test/data_align_256'
#test_list = 'test.txt'

lfw_root = ‘/facedata/lfw-align-128'
lfw_test_list = './lfw_test_pair.txt'

checkpoints_path = 'checkpoint'
load_model_path = 'models/resnet18.pth'
test_model_path = 'checkpoint/resnet18_110.pth'
save_interval = 5

train_batch_size = 256  # batch size
test_batch_size = 60

input_shape = (1, 128, 128)

optimizer = 'sgd'

use_gpu = True  # use GPU or not
gpu_id = '0, 1'
num_workers = 4  # how many workers for loading data
print_freq = 100  # print info every N batch

debug_file = '/tmp/debug'  # if os.path.exists(debug_file): enter ipdb
result_file = 'result.csv'

max_epoch = 50
lr = 1e-1  # initial learning rate
lr_step = 10
lr_decay = 0.95  # when val_loss increase, lr = lr*lr_decay
weight_decay = 5e-4

[lypliuyouping]

total time is 254.24768543243408, average time is 0.9892906047954634 lfw face verification accuracy: 0.9095 threshold: 0.37753257 Sat Dec 23 08:19:27 2023 train epoch 2 iter 10 0.08301005559180279 iters/s loss 12.163952827453613 acc 0.0234375 Sat Dec 23 08:35:10 2023 train epoch 2 iter 110 0.10600735235136766 iters/s loss 11.601116180419922 acc 0.02734375 Sat Dec 23 08:51:06 2023 train epoch 2 iter 210 0.10467295813176283 iters/s loss 11.698826789855957 acc 0.02734375 Sat Dec 23 09:07:12 2023 train epoch 2 iter 310 0.10353925427018165 iters/s loss 11.612323760986328 acc 0.0390625 Sat Dec 23 09:23:40 2023 train epoch 2 iter 410 0.10121389428942351 iters/s loss 11.697545051574707 acc 0.04296875 Sat Dec 23 09:39:57 2023 train epoch 2 iter 510 0.10231825089000418 iters/s loss 11.737585067749023 acc 0.046875 Sat Dec 23 09:57:14 2023 train epoch 2 iter 610 0.09639180206759532 iters/s loss 11.6640625 acc 0.03515625

训练数据是CASIA-WebFace 10575的那份数据

以下是配置文件： class Config(object): env = 'default' backbone = 'resnet18' classify = 'softmax' num_classes = 13938 #10575 metric = 'arc_margin' easy_margin = False use_se = False loss = 'focal_loss'

display = False
finetune = False

train_root = /facedata/CASIA-WebFace_align/'
train_list = '/img_train.txt'
#val_list = '/data/Datasets/webface/val_data_13938.txt'

#test_root = '/data1/Datasets/anti-spoofing/test/data_align_256'
#test_list = 'test.txt'

lfw_root = ‘/facedata/lfw-align-128'
lfw_test_list = './lfw_test_pair.txt'

checkpoints_path = 'checkpoint'
load_model_path = 'models/resnet18.pth'
test_model_path = 'checkpoint/resnet18_110.pth'
save_interval = 5

train_batch_size = 256  # batch size
test_batch_size = 60

input_shape = (1, 128, 128)

optimizer = 'sgd'

use_gpu = True  # use GPU or not
gpu_id = '0, 1'
num_workers = 4  # how many workers for loading data
print_freq = 100  # print info every N batch

debug_file = '/tmp/debug'  # if os.path.exists(debug_file): enter ipdb
result_file = 'result.csv'

max_epoch = 50
lr = 1e-1  # initial learning rate
lr_step = 10
lr_decay = 0.95  # when val_loss increase, lr = lr*lr_decay
weight_decay = 5e-4

你好，请问这个问题解决了吗

[hwd-code]

请问你们的训练数据集和list文件是哪里获取的，我网上下载的webface只有图片

[15380831711]

请问你们的训练数据集和list文件是哪里获取的，我网上下载的webface只有图片

稍微改下清洗脚本