Paper: https://arxiv.org/abs/2102.06171.pdf
Original code: https://github.com/deepmind/deepmind-research/tree/master/nfnets
I recommend using Docker to run the code:
docker build -t nfnets/imagenet:latest --build-arg USER_ID=$(id -u) --build-arg GROUP_ID=$(id -g) .
To train NFNets on imagenet dataset:
docker run --rm -it --gpus all -v $(pwd):/tf -p 8889:8888 -p 6006:6006 nfnets/imagenet:latest python train.py --variant F0 --batch_size 4096 --num_epochs 360
Please see the train.py module to get more arguments.
Pre-trained weights have been converted to be compatible with my models' implementation. You can download them from here
To evaluate NFNets on test set of imagenet dataset:
docker run --rm -it --gpus all -v $(pwd):/tf -p 8889:8888 -p 6006:6006 nfnets/imagenet:latest python evaluate_imagenet.py --variant F0 --batch_size 50
You can also check the notebook in the repo showing how to run an NFNet to classify an image.
- WSConv2d
- Clipping Gradient module
- Documentation
- NFNets
- NF-ResNets
- Update pretrained weights
- How to find-tune
To cite the original paper, use:
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:},
year={2021}
}