# ResNeXt A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width. ## How do I use this model on an image? To load a pretrained model: ```python import timm model = timm.create_model('resnext101_32x8d', 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. `resnext101_32x8d`. 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('resnext101_32x8d', pretrained=True, num_classes=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 @article{DBLP:journals/corr/XieGDTH16, author = {Saining Xie and Ross B. Girshick and Piotr Doll{\'{a}}r and Zhuowen Tu and Kaiming He}, title = {Aggregated Residual Transformations for Deep Neural Networks}, journal = {CoRR}, volume = {abs/1611.05431}, year = {2016}, url = {http://arxiv.org/abs/1611.05431}, archivePrefix = {arXiv}, eprint = {1611.05431}, timestamp = {Mon, 13 Aug 2018 16:45:58 +0200}, biburl = {https://dblp.org/rec/journals/corr/XieGDTH16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <!-- Type: model-index Collections: - Name: ResNeXt Paper: Title: Aggregated Residual Transformations for Deep Neural Networks URL: https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep Models: - Name: resnext101_32x8d In Collection: ResNeXt Metadata: FLOPs: 21180417024 Parameters: 88790000 File Size: 356082095 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Global Average Pooling - Grouped Convolution - Max Pooling - ReLU - ResNeXt Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: resnext101_32x8d Crop Pct: '0.875' Image Size: '224' Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L877 Weights: https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.3% Top 5 Accuracy: 94.53% - Name: resnext50_32x4d In Collection: ResNeXt Metadata: FLOPs: 5472648192 Parameters: 25030000 File Size: 100435887 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Global Average Pooling - Grouped Convolution - Max Pooling - ReLU - ResNeXt Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: resnext50_32x4d Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L851 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50_32x4d_ra-d733960d.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.79% Top 5 Accuracy: 94.61% - Name: resnext50d_32x4d In Collection: ResNeXt Metadata: FLOPs: 5781119488 Parameters: 25050000 File Size: 100515304 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Global Average Pooling - Grouped Convolution - Max Pooling - ReLU - ResNeXt Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: resnext50d_32x4d Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L869 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.67% Top 5 Accuracy: 94.87% - Name: tv_resnext50_32x4d In Collection: ResNeXt Metadata: FLOPs: 5472648192 Parameters: 25030000 File Size: 100441675 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Global Average Pooling - Grouped Convolution - Max Pooling - ReLU - ResNeXt Block - Residual Connection - Softmax Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet ID: tv_resnext50_32x4d LR: 0.1 Epochs: 90 Crop Pct: '0.875' LR Gamma: 0.1 Momentum: 0.9 Batch Size: 32 Image Size: '224' LR Step Size: 30 Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L842 Weights: https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 77.61% Top 5 Accuracy: 93.68% -->