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Single-file Pytorch RegNet, with pretrained weights

A single-file, modularized implementation of RegNet as introduced in [I. Radosavovic, R. P. Kosaraju, R. Girshick, K. He, and P. Dollár]: Designing Network Design Spaces. (CVPR), 2020

Original implementation: facebookresearch/pycls

Pretrained weights are taken from original implementation.

Usage

The example below creates an RegNetY-200MF model that takes 3-channel RGB image as input and outputs distribution over 50 classes, model weights are initialized with weights pretrained on ImageNet dataset:

from regnet import RegNet

model = RegNet('RegNetY-200MF',
                in_channels=3,
                num_classes=50,
                pretrained=True
                )
# inp - tensor of shape [batch_size, in_channels, image_height, image_width]
inp = torch.randn([10, 3, 224, 224])

# to get predictions:
pred = model(inp)
print('out shape:', pred.shape)
# >>> out shape: torch.Size([10, 50])

# to extract features:
features = model.get_features(inp)
for i, feature in enumerate(features):
    print('feature %d shape:' % i, feature.shape)
# >>> feature 0 shape: torch.Size([10, 24, 56, 56])
# >>> feature 1 shape: torch.Size([10, 56, 28, 28])
# >>> feature 2 shape: torch.Size([10, 152, 14, 14])
# >>> feature 3 shape: torch.Size([10, 368, 7, 7])

pred is now a tensor containing output logits while features is a list of 4 tensors representing outputs of model's 4 intermediate convolutional stages.

Parameters

  • name, (str) - Model name, e.g. 'RegNetY-200MF'
  • in_channels, (int), (Default=3) - Number of channels in input image
  • num_classes, (int), (Default=1000) - Number of output classes
  • bn_eps, (float), (Default=1e-5) - Batch normalizaton epsilon
  • bn_momentum, (float), (Default=0.1) - Batch normalization momentum
  • relu_inplace, (bool), (Default=False) - relu_inplace parameter for ReLU activation
  • pretrained, (bool), (Default=False) - Whether to initialize model with weights pretrained on ImageNet dataset
  • progress, (bool), (Default=False) - Show progress bar when downloading pretrained weights.

Evaluation

A simple script to evaluate pretrained models against Imagenet validation set is provided in imagenet_eval.ipynb.

Accuracy achieved by models with pre-trained weights against ImageNet dataset:

Model Accuracy, %
RegNetX-200MF 69.718
RegNetX-400MF 73.47
RegNetX-600MF 74.754
RegNetX-800MF 75.662
RegNetX-1.6GF 77.632
RegNetX-3.2GF 78.85
RegNetX-4.0GF 79.06
RegNetX-6.4GF 79.556
RegNetX-8.0GF 79.784
RegNetX-12GF 80.138
RegNetX-16GF 80.56
RegNetX-32GF 80.776
RegNetY-200MF 71.356
RegNetY-400MF 74.92
RegNetY-600MF 76.182
RegNetY-800MF 77.106
RegNetY-1.6GF 78.68
RegNetY-3.2GF 79.58
RegNetY-4.0GF 80.112
RegNetY-6.4GF 80.512
RegNetY-8.0GF 80.614
RegNetY-12GF 81.052
RegNetY-16GF 81.104
RegNetY-32GF 81.464

Requirements

  • Python v3.5+
  • Pytorch v1.4+