ReXNet can be trained with any PyTorch training codes including ImageNet training in PyTorch with the model file and proper arguments. Since the provided model file is not complicated, we simply convert the model to train a ReXNet in other frameworks like MXNet.

return _create_rexnet('rexnetr_150', pretrained, width_mult=1.5, ch_div=8, **kwargs)

Official Pytorch implementation of ReXNet (Rank eXpansion Network) with pretrained models - clovaai/rexnet

Official Pytorch implementation of ReXNet (Rank eXpansion Network) with pretrained models - clovaai/rexnet

A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` - https://arxiv.org/abs/2007.00992 Adapted from original impl at https://github.com/clovaai/rexnet

A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` - https://arxiv.org/abs/2007.00992 Adapted from original impl at https://github.com/clovaai/rexnet

Implementation of ReXNet-tiny model using PyTorch. Contribute to ysbsb/ReXNet-PyTorch development by creating an account on GitHub.

Feb 2, 2023 · I noticed that a timm model gets any size of an input image without raising an error. model = load_model("rexnet_150", 0) model.eval() a = torch.rand(1,3,230,300) print(model(a).shape) # ...

RexNet Rank Expansion Networks (ReXNets) follow a set of new design principles for designing bottlenecks in image classification models. Authors refine each layer by 1) expanding the input channel size of the convolution layer and 2) replacing the ReLU6s .

Unofficial TensorFlow implementation of "ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network".