Rexnet1 0x

🚀 ReXNet-1.0x model

A pre - trained model for wildfire binary classification, leveraging the ReXNet architecture.

🚀 Quick Start

The ReXNet-1.0x model is pre - trained on a dataset for wildfire binary classification (soon to be shared). It was introduced in this paper.

✨ Features

The core idea of the author is to add a customized Squeeze - Excitation layer in the residual blocks that will prevent channel redundancy.

📦 Installation

Prerequisites

Python 3.6 (or higher) and pip/conda are required to install PyroVision.

Latest stable release

You can install the last stable release of the package using pypi as follows:

pip install pyrovision

or using conda:

conda install -c pyronear pyrovision

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/pyronear/pyro-vision.git
pip install -e pyro-vision/.

💻 Usage Examples

Basic Usage

from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from pyrovision.models import model_from_hf_hub

model = model_from_hf_hub("pyronear/rexnet1_0x").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)

📚 Documentation

Citation

Original paper

@article{DBLP:journals/corr/abs-2007-00992,
  author    = {Dongyoon Han and
               Sangdoo Yun and
               Byeongho Heo and
               Young Joon Yoo},
  title     = {ReXNet: Diminishing Representational Bottleneck on Convolutional Neural
               Network},
  journal   = {CoRR},
  volume    = {abs/2007.00992},
  year      = {2020},
  url       = {https://arxiv.org/abs/2007.00992},
  eprinttype = {arXiv},
  eprint    = {2007.00992},
  timestamp = {Mon, 06 Jul 2020 15:26:01 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2007-00992.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}
}

📄 License

This project is licensed under the Apache-2.0 license.