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https://github.com/conansherry/detectron2
detectron2 windows build
https://github.com/conansherry/detectron2
conda detectron2 visual-studio vs win windows
Last synced: 23 days ago
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detectron2 windows build
- Host: GitHub
- URL: https://github.com/conansherry/detectron2
- Owner: conansherry
- License: apache-2.0
- Created: 2019-11-02T05:29:17.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-06-18T20:55:31.000Z (over 3 years ago)
- Last Synced: 2024-08-01T03:45:12.888Z (4 months ago)
- Topics: conda, detectron2, visual-studio, vs, win, windows
- Language: Python
- Homepage:
- Size: 1.74 MB
- Stars: 223
- Watchers: 9
- Forks: 54
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
### Requirements
- Python >= 3.6(Conda)
- PyTorch 1.3
- [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
You can install them together at [pytorch.org](https://pytorch.org) to make sure of this.
- OpenCV, needed by demo and visualization
- [fvcore](https://github.com/facebookresearch/fvcore/): `pip install git+https://github.com/facebookresearch/fvcore`
- pycocotools: `pip install cython; pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI`
- VS2019(no test in older version)/CUDA10.1(no test in older version)
### several files must be changed by manually.
```
file1:
{your evn path}\Lib\site-packages\torch\include\torch\csrc\jit\argument_spec.h
example:
{C:\Miniconda3\envs\py36}\Lib\site-packages\torch\include\torch\csrc\jit\argument_spec.h(190)
static constexpr size_t DEPTH_LIMIT = 128;
change to -->
static const size_t DEPTH_LIMIT = 128;
file2:
{your evn path}\Lib\site-packages\torch\include\pybind11\cast.h
example:
{C:\Miniconda3\envs\py36}\Lib\site-packages\torch\include\pybind11\cast.h(1449)
explicit operator type&() { return *(this->value); }
change to -->
explicit operator type&() { return *((type*)this->value); }
```
### Build detectron2
After having the above dependencies, run:
```
conda activate {your env}
"C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat"
git clone https://github.com/conansherry/detectron2
cd detectron2
python setup.py build develop
```
Note: you may need to rebuild detectron2 after reinstalling a different build of PyTorch.
Detectron2 is Facebook AI Research's next generation software system
that implements state-of-the-art object detection algorithms.
It is a ground-up rewrite of the previous version,
[Detectron](https://github.com/facebookresearch/Detectron/),
and it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
### What's New
* It is powered by the [PyTorch](https://pytorch.org) deep learning framework.
* Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
* Can be used as a library to support [different projects](projects/) on top of it.
We'll open source more research projects in this way.
* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).
See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
to see more demos and learn about detectron2.
## Installation
See [INSTALL.md](INSTALL.md).
## Quick Start
See [GETTING_STARTED.md](GETTING_STARTED.md),
or the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5).
Learn more at our [documentation](https://detectron2.readthedocs.org).
And see [projects/](projects/) for some projects that are built on top of detectron2.
## Model Zoo and Baselines
We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).
## License
Detectron2 is released under the [Apache 2.0 license](LICENSE).
## Citing Detectron
If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.
```BibTeX
@misc{wu2019detectron2,
author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and
Wan-Yen Lo and Ross Girshick},
title = {Detectron2},
howpublished = {\url{https://github.com/facebookresearch/detectron2}},
year = {2019}
}
```