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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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- pyronear/openfire |
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--- |
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# ReXNet-1.0x model |
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Pretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf). |
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## Model description |
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The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy. |
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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 PyroVision. |
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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/pyrovision/) as follows: |
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```shell |
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pip install pyrovision |
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``` |
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or using [conda](https://anaconda.org/pyronear/pyrovision): |
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```shell |
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conda install -c pyronear pyrovision |
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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/pyronear/pyro-vision.git |
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pip install -e pyro-vision/. |
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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 pyrovision.models import model_from_hf_hub |
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model = model_from_hf_hub("pyronear/rexnet1_0x").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-2007-00992, |
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author = {Dongyoon Han and |
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Sangdoo Yun and |
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Byeongho Heo and |
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Young Joon Yoo}, |
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title = {ReXNet: Diminishing Representational Bottleneck on Convolutional Neural |
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Network}, |
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journal = {CoRR}, |
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volume = {abs/2007.00992}, |
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year = {2020}, |
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url = {https://arxiv.org/abs/2007.00992}, |
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eprinttype = {arXiv}, |
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eprint = {2007.00992}, |
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timestamp = {Mon, 06 Jul 2020 15:26:01 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2007-00992.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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