# RegNetY **RegNetY** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure): $$ u\_{j} = w\_{0} + w\_{a}\cdot{j} $$ For **RegNetX** authors have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \leq d \leq 28$, and $w\_{m} \geq 2$ (the width multiplier). For **RegNetY** authors make one change, which is to include [Squeeze-and-Excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block). ## How do I use this model on an image? To load a pretrained model: ```py >>> import timm >>> model = timm.create_model('regnety_002', pretrained=True) >>> model.eval() ``` To load and preprocess the image: ```py >>> 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: ```py >>> 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: ```py >>> # 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. `regnety_002`. 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](../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). ```py >>> model = timm.create_model('regnety_002', 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](../scripts) for training a new model afresh. ## Citation ```BibTeX @misc{radosavovic2020designing, title={Designing Network Design Spaces}, author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár}, year={2020}, eprint={2003.13678}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- Type: model-index Collections: - Name: RegNetY Paper: Title: Designing Network Design Spaces URL: https://paperswithcode.com/paper/designing-network-design-spaces Models: - Name: regnety_002 In Collection: RegNetY Metadata: FLOPs: 255754236 Parameters: 3160000 File Size: 12782926 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_002 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 1024 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L409 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 70.28% Top 5 Accuracy: 89.55% - Name: regnety_004 In Collection: RegNetY Metadata: FLOPs: 515664568 Parameters: 4340000 File Size: 17542753 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_004 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 1024 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L415 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 74.02% Top 5 Accuracy: 91.76% - Name: regnety_006 In Collection: RegNetY Metadata: FLOPs: 771746928 Parameters: 6060000 File Size: 24394127 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_006 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 1024 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L421 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 75.27% Top 5 Accuracy: 92.53% - Name: regnety_008 In Collection: RegNetY Metadata: FLOPs: 1023448952 Parameters: 6260000 File Size: 25223268 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_008 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 1024 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L427 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 76.32% Top 5 Accuracy: 93.07% - Name: regnety_016 In Collection: RegNetY Metadata: FLOPs: 2070895094 Parameters: 11200000 File Size: 45115589 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_016 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 1024 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L433 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 77.87% Top 5 Accuracy: 93.73% - Name: regnety_032 In Collection: RegNetY Metadata: FLOPs: 4081118714 Parameters: 19440000 File Size: 78084523 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_032 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L439 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 82.01% Top 5 Accuracy: 95.91% - Name: regnety_040 In Collection: RegNetY Metadata: FLOPs: 5105933432 Parameters: 20650000 File Size: 82913909 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_040 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L445 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.23% Top 5 Accuracy: 94.64% - Name: regnety_064 In Collection: RegNetY Metadata: FLOPs: 8167730444 Parameters: 30580000 File Size: 122751416 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_064 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L451 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.73% Top 5 Accuracy: 94.76% - Name: regnety_080 In Collection: RegNetY Metadata: FLOPs: 10233621420 Parameters: 39180000 File Size: 157124671 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_080 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L457 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.87% Top 5 Accuracy: 94.83% - Name: regnety_120 In Collection: RegNetY Metadata: FLOPs: 15542094856 Parameters: 51820000 File Size: 207743949 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_120 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L463 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.38% Top 5 Accuracy: 95.12% - Name: regnety_160 In Collection: RegNetY Metadata: FLOPs: 20450196852 Parameters: 83590000 File Size: 334916722 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_160 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L469 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.28% Top 5 Accuracy: 94.97% - Name: regnety_320 In Collection: RegNetY Metadata: FLOPs: 41492618394 Parameters: 145050000 File Size: 580891965 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU - Squeeze-and-Excitation Block Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnety_320 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L475 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.8% Top 5 Accuracy: 95.25% -->