Facenet implementation using Tensorflow 2.x
Using Inception - V3 to find the n-dimensional embeddings which could represent a face such that it is distinguishable from faces of other people. Triplet loss is used to minimise the intra-class variance and increase the inter-class vairance. Three variations of triplet losses are used which can be found at src/triplet_loss.py.
Triplet Loss: A straight-forward procedure which finds triplets by iterating through all triplets formed.Hard Triplet Loss: A procedure which chooses triplet pairs such that distance b/w -ve and anchor is less and the distance b/w anchor and +ve is more.Adaptive Triplet Loss: The main idea to correct the triplet selection bias, so we try to minize the distribution shift between the batch and the tripet set.
train.py: Creates a trainer object for maintaing checkpoints, logs, train loop and args.src/data.py: Creates tf.data.dataset from tfrecords in the specified directory.src/model.py: Creates model class which gives embeddings from a image.src/params.py: Creates paramters class from a json in the hyperparameters directory.src/triplet_loss.py: Loss functions and loss utility functions for for triplet loss.
Train from scratch
python train.py --params_dir ./hyperparameters/batch_all.json --data_dir ./data/
--log_dir ./.logs/ --ckpt_dir ./.ckpt/ --restore 0
Restore from previous checkpoint
python train.py --params_dir --restore 1
- Face-Recognition-Triplet-Loss-on-Inception-v3 - Clean and well documented implementation of Triplet loss implementation.
- Face Recognition using Tensorflow - The evaluation script on LFW is used and modified to use TF2. Ideas from the documentation and code are also used.
F. Schroff, D. Kalenichenko and J. Philbin, "FaceNet: A unified embedding for face recognition and clustering,
" 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015,
pp. 815-823, doi: 10.1109/CVPR.2015.7298682.
Yu B., Liu T., Gong M., Ding C., Tao D. (2018) Correcting the Triplet Selection Bias for Triplet Loss.
In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018.
Lecture Notes in Computer Science, vol 11210. Springer, Cham. https://doi.org/10.1007/978-3-030-01231-1_5