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---
license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- pyronear/openfire
---
# ResNet-18 model
Pretrained on a dataset for wildfire binary classification (soon to be shared).
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
## Installation
### Prerequisites
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.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pyrovision/) as follows:
```shell
pip install pyrovision
```
or using [conda](https://anaconda.org/pyronear/pyrovision):
```shell
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](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/pyronear/pyro-vision.git
pip install -e pyro-vision/.
```
## Usage instructions
```python
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/resnet18").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
```bibtex
@article{DBLP:journals/corr/HeZRS15,
author = {Kaiming He and
Xiangyu Zhang and
Shaoqing Ren and
Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {CoRR},
volume = {abs/1512.03385},
year = {2015},
url = {http://arxiv.org/abs/1512.03385},
eprinttype = {arXiv},
eprint = {1512.03385},
timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{chintala_torchvision_2017,
author = {Chintala, Soumith},
month = {4},
title = {{Torchvision}},
url = {https://github.com/pytorch/vision},
year = {2017}
}
``` |