Tripletloss(Facenet) for few shot learning

For few-shot learning tasks like IDCard<->Camera face verification(identification), we only have two face images for each person in most cases for training. Under such situation, metric learning approaches can be tried such as tripletloss.

STEPS:

1. Prepare insightface ‘.rec’ dataset from your IDCard/camera face images.
2. Finetuning pretrained models with tripletloss, for example:

CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_triplet.py --data-dir $DATA_DIR \
  --network "$NETWORK" --lr 0.005 --pretrained "$PRETRAINED" --per-batch-size 60 

We use GPU to do the semi-hard mining so training will be fast.

RESULTS:

We have a private IDCard/Camera face image dataset with 220K identities. Each person has two or more photos, one from IDCard and the others from camera. Split it as 8-2 for training(176K IDs) and testing(45K IDs). We report top1 accuracy and TAR vs FAR for 1:N identification task(N=45K). (Note that we do not use idcard training data in Model-A and Model-B)

	DESC.	Rank-1	TAR@FAR=1e-3	TAR@FAR=1e-4
Model-A1	LResNet100E trained on ms1m-v1 with Softmax loss	26.9%	0.3%	0.06%
Model-A2	LResNet100E trained on ms1m-v1 with ArcFace loss	70.7%	17%	8%
Model-A3	LResNet100E trained on ms1m-v2 with ArcFace loss	76.8%	21%	9%
Model-B	LResNet100E trained on (ms1m-v2+Glint-Asia) with ArcFace loss	82.4%	33%	16%
Model-C	Triplet-loss finetuning on Model-B	95.2%(still ongoing)	78%	26%

[stupiding]

Will you release the emore + Glin-Asia data, or show how to combine the two?

[harshraj22]

where is train_triplet.py file located ?