# (Gluon) ResNet **Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks. The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html). ## How do I use this model on an image? To load a pretrained model: ```python import timm model = timm.create_model('gluon_resnet101_v1b', 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. `gluon_resnet101_v1b`. 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('gluon_resnet101_v1b', 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/HeZRS15, author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, title = {Deep Residual Learning for Image Recognition}, journal = {CoRR}, volume = {abs/1512.03385}, year = {2015}, url = {http://arxiv.org/abs/1512.03385}, archivePrefix = {arXiv}, eprint = {1512.03385}, timestamp = {Wed, 17 Apr 2019 17:23:45 +0200}, biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <!-- Type: model-index Collections: - Name: Gloun ResNet Paper: Title: Deep Residual Learning for Image Recognition URL: https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition Models: - Name: gluon_resnet101_v1b In Collection: Gloun ResNet Metadata: FLOPs: 10068547584 Parameters: 44550000 File Size: 178723172 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet101_v1b Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L89 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.3% Top 5 Accuracy: 94.53% - Name: gluon_resnet101_v1c In Collection: Gloun ResNet Metadata: FLOPs: 10376567296 Parameters: 44570000 File Size: 178802575 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet101_v1c Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L113 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.53% Top 5 Accuracy: 94.59% - Name: gluon_resnet101_v1d In Collection: Gloun ResNet Metadata: FLOPs: 10377018880 Parameters: 44570000 File Size: 178802755 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet101_v1d Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L138 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.4% Top 5 Accuracy: 95.02% - Name: gluon_resnet101_v1s In Collection: Gloun ResNet Metadata: FLOPs: 11805511680 Parameters: 44670000 File Size: 179221777 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet101_v1s Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L166 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.29% Top 5 Accuracy: 95.16% - Name: gluon_resnet152_v1b In Collection: Gloun ResNet Metadata: FLOPs: 14857660416 Parameters: 60190000 File Size: 241534001 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet152_v1b Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L97 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.69% Top 5 Accuracy: 94.73% - Name: gluon_resnet152_v1c In Collection: Gloun ResNet Metadata: FLOPs: 15165680128 Parameters: 60210000 File Size: 241613404 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet152_v1c Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L121 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.91% Top 5 Accuracy: 94.85% - Name: gluon_resnet152_v1d In Collection: Gloun ResNet Metadata: FLOPs: 15166131712 Parameters: 60210000 File Size: 241613584 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet152_v1d Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L147 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.48% Top 5 Accuracy: 95.2% - Name: gluon_resnet152_v1s In Collection: Gloun ResNet Metadata: FLOPs: 16594624512 Parameters: 60320000 File Size: 242032606 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet152_v1s Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L175 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 81.02% Top 5 Accuracy: 95.42% - Name: gluon_resnet18_v1b In Collection: Gloun ResNet Metadata: FLOPs: 2337073152 Parameters: 11690000 File Size: 46816736 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet18_v1b Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L65 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 70.84% Top 5 Accuracy: 89.76% - Name: gluon_resnet34_v1b In Collection: Gloun ResNet Metadata: FLOPs: 4718469120 Parameters: 21800000 File Size: 87295112 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet34_v1b Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L73 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 74.59% Top 5 Accuracy: 92.0% - Name: gluon_resnet50_v1b In Collection: Gloun ResNet Metadata: FLOPs: 5282531328 Parameters: 25560000 File Size: 102493763 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet50_v1b Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L81 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 77.58% Top 5 Accuracy: 93.72% - Name: gluon_resnet50_v1c In Collection: Gloun ResNet Metadata: FLOPs: 5590551040 Parameters: 25580000 File Size: 102573166 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet50_v1c Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L105 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.01% Top 5 Accuracy: 93.99% - Name: gluon_resnet50_v1d In Collection: Gloun ResNet Metadata: FLOPs: 5591002624 Parameters: 25580000 File Size: 102573346 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet50_v1d Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L129 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.06% Top 5 Accuracy: 94.46% - Name: gluon_resnet50_v1s In Collection: Gloun ResNet Metadata: FLOPs: 7019495424 Parameters: 25680000 File Size: 102992368 Architecture: - 1x1 Convolution - Batch Normalization - Bottleneck Residual Block - Convolution - Global Average Pooling - Max Pooling - ReLU - Residual Block - Residual Connection - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: gluon_resnet50_v1s Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L156 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.7% Top 5 Accuracy: 94.25% -->