PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation

Related tags

Deep Learning pytorch biometrics face-recognition knowledge-distillation neural-architecture-search pocketnet
Overview

PocketNet

This is the official repository of the paper:

PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation

Paper on arxiv: arxiv

evaluation

Face recognition model training

Download MS1MV2 dataset from insightface on strictly follow the licence distribution

Extract the dataset and place it in the data folder

Rename the config/config_xxxxxx.py to config/config.py

  1. Train PocketNet with ArcFace loss
    • ./train.sh
  2. Train PocketNet with template knowledge distillation
    • ./train_kd.sh
  3. Train PocketNet with multi-step template knowledge distillation
    • ./train_kd.sh
Model Parameters (M) configuration log pretrained model
PocketNetS-128 0.92 Config log Pretrained-model
PocketNetS-256 0.99 Config log Pretrained-model
PocketNetM-128 1.68 Config log Pretrained-model
PocketNetM-256 1.75 Config log Pretrained-model

Differentiable architecture search training

To-do

  • Add pretrained model
  • Training configuration
  • Add NAS code
  • Add evaluation results
  • Add requirements

If you use any of the provided code in this repository, please cite the following paper:

@misc{boutros2021pocketnet,
      title={PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation}, 
      author={Fadi Boutros and Patrick Siebke and Marcel Klemt and Naser Damer and Florian Kirchbuchner and Arjan Kuijper},
      year={2021},
      eprint={2108.10710},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
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Comments
  • Inference time

    Inference time

    Firstly, I really admire all your works (PocketNet, QuantFace, MixFaceNets).

    I have a concern, the inference time is extremely slow (even with GPU support), any suggestions on how to make it faster?

    Thanks!

    opened by HamDan1999 4
  • training log without KD

    training log without KD

    would you mind releasing the training log of PocketNetS-128 (no KD)? I'm curious about how loss decrease when training without KD, comparing with that with KD.

    opened by HHCorp 2
  • Issue about setting of training data and validation data

    Issue about setting of training data and validation data

    Hello, in the code of architecture search, ie DART/searchs/search.py, I found that the difference between the training data and the validation data is whether there is random cropping, is that right? 49 # get dataset and meta info 50 input_size, input_channels, n_classes, train_data = dataset.get_train_dataset(cfg.root, cfg.dataset) 51 val_data = dataset.get_dataset_without_crop(cfg.root, cfg.dataset) I am also studying the application of NAS in recognition tasks recently, but I am confused about how to set training data and validation data because I found that the conventional setting seems to be unable to be applied to the NAS. Can you tell me your understanding of this? Thanks a lot!!!

    opened by huangjiyi 0
  • where the pre-trained teacher model?

    where the pre-trained teacher model?

    `backbone_teacher = iresnet100(num_features=cfg.embedding_size).to(local_rank) try: backbone_teacher_pth = os.path.join(cfg.teacher_pth, str((epoch + 1)*11372) + "backbone.pth") backbone_teacher.load_state_dict(torch.load(backbone_teacher_pth, map_location=torch.device(local_rank)))

            if rank == 0:
            logging.info("backbone teacher loaded for epoch {} successfully!".format(epoch))
    except (FileNotFoundError, KeyError, IndexError, RuntimeError):
        logging.info("load teacher backbone for epoch {} init, failed!".format(epoch))
        break`

    I can not find the teacher model.

    opened by xiakj 2
Owner
Fadi Boutros
Fadi Boutros
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