# 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](https://www.paperswithcode.com/method/relu6). ## How do I use this model on an image? To load a pretrained model: ```python import timm model = timm.create_model('rexnet_100', pretrained=True) model.eval() ``` To load and preprocess the image: ```python import urllib from PIL import Image from timm.data import resolve_data_config from timm.data.transforms_factory import create_transform config = resolve_data_config({}, model=model) transform = create_transform(**config) url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") urllib.request.urlretrieve(url, filename) img = Image.open(filename).convert('RGB') tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```python import torch with torch.no_grad(): out = model(tensor) probabilities = torch.nn.functional.softmax(out[0], dim=0) print(probabilities.shape) # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```python # Get imagenet class mappings url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") urllib.request.urlretrieve(url, filename) with open("imagenet_classes.txt", "r") as f: categories = [s.strip() for s in f.readlines()] # Print top categories per image top5_prob, top5_catid = torch.topk(probabilities, 5) for i in range(top5_prob.size(0)): print(categories[top5_catid[i]], top5_prob[i].item()) # prints class names and probabilities like: # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `rexnet_100`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```python model = timm.create_model('rexnet_100', pretrained=True).reset_classifier(NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh. ## Citation ```BibTeX @misc{han2020rexnet, title={ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network}, author={Dongyoon Han and Sangdoo Yun and Byeongho Heo and YoungJoon Yoo}, year={2020}, eprint={2007.00992}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```