Framework-agnostic library for checking array shapes at runtime.
JAX ResNet - Implementations and Checkpoints for ResNet Variants
A Flax (Linen) implementation of ResNet (He et al. 2015), Wide ResNet (Zagoruyko & Komodakis 2016), ResNeXt (Xie et al. 2017), ResNet-D (He et al. 2020), and ResNeSt (Zhang et al. 2020). The code is modular so you can mix and match the various stem, residual, and bottleneck implementations.
You can install this package from PyPI:
pip install jax-resnet
Or directly from GitHub:
pip install --upgrade git+https://github.com/n2cholas/jax-resnet.git
See the bottom of
jax-resnet/resnet.py for the available aliases/options for
the ResNet variants (all models are in Flax)
Pretrained checkpoints from
torch.hub are available for the
- ResNet [18, 34, 50, 101, 152]
- WideResNet [50, 101]
- ResNeXt [50, 101]
- ResNeSt [50-Fast, 50, 101, 200, 269]
The models are
to have the same intermediate activations and outputs as the
implementations, except ResNeSt-50 Fast, whose activations don't match exactly
but the final accuracy does.
A pretrained checkpoint for ResNetD-50 is available from fast.ai. The activations do not match exactly, but the final accuracy matches.
import jax.numpy as jnp from jax_resnet import pretrained_resnest ResNeSt50, variables = pretrained_resnest(50) model = ResNeSt50() out = model.apply(variables, jnp.ones((32, 224, 224, 3)), # ImageNet sized inputs. mutable=False) # Ensure `batch_stats` aren't updated.
You must install PyTorch yourself (instructions) to use these functions.
To extract a subset of the model, you can use
slice_variables function (found in in
allows you to extract the corresponding subset of the variables dict. Check out
that docstring for more information.
The top 1 and top 5 accuracies reported below are on the ImageNet2012 validation split. The data was preprocessed as in the official PyTorch example.
|Model||Size||Top 1||Top 5|
The ResNeSt validation data was preprocessed as in zhang1989/ResNeSt.
|Model||Size||Crop Size||Top 1||Top 5|
- Deep Residual Learning for Image Recognition. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. arXiv 2015.
- Wide Residual Networks. Sergey Zagoruyko, Nikos Komodakis. BMVC 2016
- Aggregated Residual Transformations for Deep Neural Networks. Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He. CVPR 2017.
- Bag of Tricks for Image Classification with Convolutional Neural Networks. Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, Mu Li. CVPR 2019.
- ResNeSt: Split-Attention Networks. Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, Alexander Smola. arXiv 2020.
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for jax_resnet-0.0.4-py2.py3-none-any.whl