VGG Dataset Statistics

Train dataset

VGG train

VGG test

Model Training

192.168.2.174:/media/harddrive1/SGAO/cstor-projects/face-recognition-pytorch/ 192.168.2.174:/media/harddrive1/SGAO/cstor-projects/face-recognition-dlib/dataset/vgg-dataset

Training 1-- Classification Problem (04/16/2018)

Mode: Classification problem, (8,361 categories) Data Loading: PyTorch built-in scheme mini-batch size: 1,792

Training 2 -- Classification Problem (04/18/2018)

Mode: Classification problem, (8,361 categories) Data Loading: Custom scheme made by SGAO mini-batch size:

DataSet Object by SGAO

192.168.2.174:/media/harddrive1/SGAO/cstor-projects/face-recognition-pytorch/dataloader_sgao/datasets_dlib.py

Dlib style: mini-batch size = (num1, num2), where num1 indicates the number of identities and num2 indicates the number of images of each of these identities.

Implementation:

• __len__ method: defined as 2 x num_total_id / num1
• __getitem__ method: returns [Tensor (size=num1 x C x H x W), Tensor (size=num2)]

Model Object by SGAO

192.168.2.174:/media/harddrive1/SGAO/cstor-projects/face-recognition-pytorch/models_sgao/net_dlib.py

Dlib Style: 29-layer ResNet with reduced number of kernels of each convolutional layer.

Loss Object by SGAO (04/18/2018)

192.168.2.174:/media/harddrive1/SGAO/cstor-projects/face-recognition-pytorch/loss_sgao/loss_sgao.py

Pair-wise hinge loss with hypterparameters which are distance and margin.

Performs hard negative mining.

TRICK: access model attributes wrapper by nn.DataParallel (PyTorch)

According to discussion:

model.module.attribute

Modify accuracy metric (04/19/2018)

if label[i] == label[j]:
    if dist(i, j) < threshold:
        num_right += 1
    else:
        num_wrong += 1
else:
    if dist(i, j) >= threshold:
        num_right += 1
    else:
        num_wrong += 1

accuracy = 100.0 * num_right / (num_right + num_wrong)

UPDATE on accuracy metric (04/20/2018)

Example,

Given: batch_size = (36, 48), then
num_same_pair = 36 x 48 x 47 / 2 = 40,608
num_diff_pair = (36 x 48) x (36 x 48) / 2 - num_same_pair = 1,451,520

When `num_diff_pair` are all correct while `num_same_pair` are all wrong,
accuracy = 1,451,520 / (40,608 + 1,451,520) = 0.972785

According to the above example, we observe the imbalance of "same identity pairs" and "different identity pairs" causes the accuracy metric to mean less.

Two approaches to remedy

• Modify the validation mini-batch size for more balanced data
• Modify the accuracy subroutine, using weighted accuracy