# RegNetX **RegNetX** 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** we have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \leq d \leq 28$, and $w\_{m} \geq 2$ (the width multiplier). ## How do I use this model on an image? To load a pretrained model: ```py >>> import timm >>> model = timm.create_model('regnetx_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. `regnetx_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('regnetx_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: RegNetX Paper: Title: Designing Network Design Spaces URL: https://paperswithcode.com/paper/designing-network-design-spaces Models: - Name: regnetx_002 In Collection: RegNetX Metadata: FLOPs: 255276032 Parameters: 2680000 File Size: 10862199 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L337 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 68.75% Top 5 Accuracy: 88.56% - Name: regnetx_004 In Collection: RegNetX Metadata: FLOPs: 510619136 Parameters: 5160000 File Size: 20841309 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L343 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 72.39% Top 5 Accuracy: 90.82% - Name: regnetx_006 In Collection: RegNetX Metadata: FLOPs: 771659136 Parameters: 6200000 File Size: 24965172 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L349 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 73.84% Top 5 Accuracy: 91.68% - Name: regnetx_008 In Collection: RegNetX Metadata: FLOPs: 1027038208 Parameters: 7260000 File Size: 29235944 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L355 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 75.05% Top 5 Accuracy: 92.34% - Name: regnetx_016 In Collection: RegNetX Metadata: FLOPs: 2059337856 Parameters: 9190000 File Size: 36988158 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L361 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 76.95% Top 5 Accuracy: 93.43% - Name: regnetx_032 In Collection: RegNetX Metadata: FLOPs: 4082555904 Parameters: 15300000 File Size: 61509573 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L367 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.15% Top 5 Accuracy: 94.09% - Name: regnetx_040 In Collection: RegNetX Metadata: FLOPs: 5095167744 Parameters: 22120000 File Size: 88844824 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L373 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.48% Top 5 Accuracy: 94.25% - Name: regnetx_064 In Collection: RegNetX Metadata: FLOPs: 8303405824 Parameters: 26210000 File Size: 105184854 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L379 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.06% Top 5 Accuracy: 94.47% - Name: regnetx_080 In Collection: RegNetX Metadata: FLOPs: 10276726784 Parameters: 39570000 File Size: 158720042 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L385 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.21% Top 5 Accuracy: 94.55% - Name: regnetx_120 In Collection: RegNetX Metadata: FLOPs: 15536378368 Parameters: 46110000 File Size: 184866342 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L391 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.61% Top 5 Accuracy: 94.73% - Name: regnetx_160 In Collection: RegNetX Metadata: FLOPs: 20491740672 Parameters: 54280000 File Size: 217623862 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L397 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.84% Top 5 Accuracy: 94.82% - Name: regnetx_320 In Collection: RegNetX Metadata: FLOPs: 40798958592 Parameters: 107810000 File Size: 431962133 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_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#L403 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.25% Top 5 Accuracy: 95.03% -->