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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- pytorch |
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- onnx |
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datasets: |
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- frgfm/imagenette |
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--- |
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# RepVGG-A0 model |
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Pretrained on [ImageNette](https://github.com/fastai/imagenette). The RepVGG architecture was introduced in [this paper](https://arxiv.org/pdf/2101.03697.pdf). |
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## Model description |
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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. |
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## Installation |
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### Prerequisites |
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Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron. |
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### Latest stable release |
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You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows: |
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```shell |
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pip install pylocron |
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``` |
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or using [conda](https://anaconda.org/frgfm/pylocron): |
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```shell |
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conda install -c frgfm pylocron |
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``` |
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### Developer mode |
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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](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*: |
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```shell |
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git clone https://github.com/frgfm/Holocron.git |
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pip install -e Holocron/. |
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``` |
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## Usage instructions |
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```python |
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from PIL import Image |
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from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize |
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from torchvision.transforms.functional import InterpolationMode |
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from holocron.models import model_from_hf_hub |
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model = model_from_hf_hub("frgfm/repvgg_a0").eval() |
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img = Image.open(path_to_an_image).convert("RGB") |
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# Preprocessing |
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config = model.default_cfg |
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transform = Compose([ |
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Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR), |
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PILToTensor(), |
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ConvertImageDtype(torch.float32), |
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Normalize(config['mean'], config['std']) |
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]) |
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input_tensor = transform(img).unsqueeze(0) |
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# Inference |
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with torch.inference_mode(): |
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output = model(input_tensor) |
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probs = output.squeeze(0).softmax(dim=0) |
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``` |
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## Citation |
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Original paper |
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```bibtex |
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@article{DBLP:journals/corr/abs-2101-03697, |
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author = {Xiaohan Ding and |
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Xiangyu Zhang and |
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Ningning Ma and |
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Jungong Han and |
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Guiguang Ding and |
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Jian Sun}, |
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title = {RepVGG: Making VGG-style ConvNets Great Again}, |
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journal = {CoRR}, |
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volume = {abs/2101.03697}, |
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year = {2021}, |
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url = {https://arxiv.org/abs/2101.03697}, |
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eprinttype = {arXiv}, |
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eprint = {2101.03697}, |
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timestamp = {Tue, 09 Feb 2021 15:29:34 +0100}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2101-03697.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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Source of this implementation |
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```bibtex |
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@software{Fernandez_Holocron_2020, |
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author = {Fernandez, François-Guillaume}, |
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month = {5}, |
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title = {{Holocron}}, |
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url = {https://github.com/frgfm/Holocron}, |
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year = {2020} |
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} |
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``` |
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