{"id":13948305,"url":"https://github.com/pyronear/pyro-vision","last_synced_at":"2025-07-27T05:31:55.285Z","repository":{"id":37899711,"uuid":"207115025","full_name":"pyronear/pyro-vision","owner":"pyronear","description":"Computer vision library for wildfire detection 🌲 Deep learning models in PyTorch \u0026 ONNX for inference on edge devices (e.g. Raspberry 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Logo](docs/source/_static/images/logo.png)\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/pyronear/pyro-vision/actions/workflows/builds.yml\"\u003e\n    \u003cimg alt=\"CI Status\" src=\"https://img.shields.io/github/actions/workflow/status/pyronear/pyro-vision/builds.yml?branch=main\u0026label=CI\u0026logo=github\u0026style=flat-square\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://pyronear.org/pyro-vision/\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/actions/workflow/status/pyronear/pyro-vision/docs.yaml?branch=main\u0026label=docs\u0026logo=read-the-docs\u0026style=flat-square\" alt=\"Documentation Status\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://codecov.io/gh/pyronear/pyro-vision\"\u003e\n    \u003cimg src=\"https://img.shields.io/codecov/c/github/pyronear/pyro-vision.svg?logo=codecov\u0026style=flat-square\" alt=\"Test coverage percentage\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://github.com/ambv/black\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square\" alt=\"black\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://www.codacy.com/gh/pyronear/pyro-vision/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=pyronear/pyro-vision\u0026amp;utm_campaign=Badge_Grade\"\u003e\u003cimg src=\"https://app.codacy.com/project/badge/Grade/6835021037b04e8da919c646c1599f29\"/\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://pypi.org/project/pyrovision/\"\u003e\n    \u003cimg src=\"https://img.shields.io/pypi/v/pyrovision.svg?logo=python\u0026logoColor=fff\u0026style=flat-square\" alt=\"PyPi Status\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://anaconda.org/pyronear/pyrovision\"\u003e\n    \u003cimg alt=\"Anaconda\" src=\"https://img.shields.io/conda/vn/pyronear/pyrovision?style=flat-square?style=flat-square\u0026logo=Anaconda\u0026logoColor=white\u0026label=conda\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://hub.docker.com/r/pyronear/pyro-vision\"\u003e\n    \u003cimg alt=\"Docker Image Version\" src=\"https://img.shields.io/docker/v/pyronear/pyro-vision?style=flat-square\u0026logo=Docker\u0026logoColor=white\u0026label=docker\"\u003e\n  \u003c/a\u003e\n  \u003cimg src=\"https://img.shields.io/pypi/pyversions/pyrovision.svg?style=flat-square\" alt=\"pyversions\"\u003e\n  \u003cimg src=\"https://img.shields.io/pypi/l/pyrovision.svg?style=flat-square\" alt=\"license\"\u003e\n\u003c/p\u003e\n\n\n# Pyrovision: wildfire early detection\n\nThe increasing adoption of mobile phones have significantly shortened the time required for firefighting agents to be alerted of a starting wildfire. In less dense areas, limiting and minimizing this duration remains critical to preserve forest areas.\n\nPyrovision aims at providing the means to create a wildfire early detection system with state-of-the-art performances at minimal deployment costs.\n\n\n\n## Quick Tour\n\n### Automatic wildfire detection in PyTorch\n\nYou can use the library like any other python package to detect wildfires as follows:\n\n```python\nfrom pyrovision.models import rexnet1_0x\nfrom torchvision import transforms\nimport torch\nfrom PIL import Image\n\n\n# Init\nnormalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n\ntf = transforms.Compose([transforms.Resize(size=(448)), transforms.CenterCrop(size=448),\n                         transforms.ToTensor(), normalize])\n\nmodel = rexnet1_0x(pretrained=True).eval()\n\n# Predict\nim = tf(Image.open(\"path/to/your/image.jpg\").convert('RGB'))\n\nwith torch.no_grad():\n    pred = model(im.unsqueeze(0))\n    is_wildfire = torch.sigmoid(pred).item() \u003e= 0.5\n```\n\n\n## Setup\n\nPython 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.\n\n### Stable release\n\nYou can install the last stable release of the package using [pypi](https://pypi.org/project/pyrovision/) as follows:\n\n```shell\npip install pyrovision\n```\n\nor using [conda](https://anaconda.org/pyronear/pyrovision):\n\n```shell\nconda install -c pyronear pyrovision\n```\n\n### Developer installation\n\nAlternatively, 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:\n\n```shell\ngit clone https://github.com/pyronear/pyro-vision.git\npip install -e pyro-vision/.\n```\n\n\n## What else\n\n### Documentation\n\nThe full package documentation is available [here](https://pyronear.org/pyro-vision/) for detailed specifications.\n\n### Demo app\n\nThe project includes a minimal demo app using [Gradio](https://gradio.app/)\n\n![demo_app](https://user-images.githubusercontent.com/26927750/179017766-326fbbff-771d-4680-a230-b2785ee89c4d.png)\n\nYou can check the live demo, hosted on :hugs: [HuggingFace Spaces](https://huggingface.co/spaces) :hugs: over here :point_down:\n[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/pyronear/vision)\n\n\n### Docker container\n\nIf you wish to deploy containerized environments, a Dockerfile is provided for you build a docker image:\n\n```shell\ndocker build . -t \u003cYOUR_IMAGE_TAG\u003e\n```\n\n### Minimal API template\n\nLooking for a boilerplate to deploy a model from PyroVision with a REST API? Thanks to the wonderful [FastAPI](https://github.com/tiangolo/fastapi) framework, you can do this easily. Follow the instructions in [`./api`](api) to get your own API running!\n\n\n### Reference scripts\n\nIf you wish to train models on your own, we provide training scripts for multiple tasks!\nPlease refer to the [`./references`](references) folder if that's the case.\n\n\n## Citation\n\nIf you wish to cite this project, feel free to use this [BibTeX](http://www.bibtex.org/) reference:\n\n```bibtex\n@misc{pyrovision2019,\n    title={Pyrovision: wildfire early detection},\n    author={Pyronear contributors},\n    year={2019},\n    month={October},\n    publisher = {GitHub},\n    howpublished = {\\url{https://github.com/pyronear/pyro-vision}}\n}\n```\n\n\n## Contributing\n\nPlease refer to [`CONTRIBUTING`](CONTRIBUTING.md) to help grow this project!\n\n\n\n## License\n\nDistributed under the Apache 2 License. See [`LICENSE`](LICENSE) for more information.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpyronear%2Fpyro-vision","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpyronear%2Fpyro-vision","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpyronear%2Fpyro-vision/lists"}