RepVGG-A0 model
Pretrained on ImageNette. The RepVGG architecture was introduced in this paper.
Model description
The core idea of the author is to distinguish the training architecture (with shortcut connections), from the inference one (a pure highway network). By designing the residual block, the training architecture can be reparametrized into a simple sequence of convolutions and non-linear activations.
Installation
Prerequisites
Python 3.6 (or higher) and pip/conda are required to install Holocron.
Latest stable release
You can install the last stable release of the package using pypi as follows:
pip install pylocron
or using conda:
conda install -c frgfm pylocron
Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source (install Git first):
git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/.
Usage instructions
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from holocron.models import model_from_hf_hub
model = model_from_hf_hub("frgfm/repvgg_a0").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
Citation
Original paper
@article{DBLP:journals/corr/abs-2101-03697,
author = {Xiaohan Ding and
Xiangyu Zhang and
Ningning Ma and
Jungong Han and
Guiguang Ding and
Jian Sun},
title = {RepVGG: Making VGG-style ConvNets Great Again},
journal = {CoRR},
volume = {abs/2101.03697},
year = {2021},
url = {https://arxiv.org/abs/2101.03697},
eprinttype = {arXiv},
eprint = {2101.03697},
timestamp = {Tue, 09 Feb 2021 15:29:34 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-03697.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Source of this implementation
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}