|
4 | 4 | **Python library with Neural Networks for Image
|
5 | 5 | Segmentation based on [PyTorch](https://pytorch.org/).**
|
6 | 6 |
|
7 |
| -[](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE) |
8 |
| -[](https://github.com/qubvel/segmentation_models.pytorch/actions/workflows/tests.yml) |
| 7 | +[](https://github.com/qubvel/segmentation_models.pytorch/blob/main/LICENSE) |
| 8 | +[](https://github.com/qubvel/segmentation_models.pytorch/actions/workflows/tests.yml) |
9 | 9 | [](https://smp.readthedocs.io/en/latest/)
|
10 | 10 | <br>
|
11 | 11 | [](https://pypi.org/project/segmentation-models-pytorch/)
|
@@ -77,8 +77,8 @@ preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')
|
77 | 77 | Congratulations! You are done! Now you can train your model with your favorite framework!
|
78 | 78 |
|
79 | 79 | ### 💡 Examples <a name="examples"></a>
|
80 |
| - - Training model for pets binary segmentation with Pytorch-Lightning [notebook](https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb) and [](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb) |
81 |
| - - Training model for cars segmentation on CamVid dataset [here](https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/cars%20segmentation%20(camvid).ipynb). |
| 80 | + - Training model for pets binary segmentation with Pytorch-Lightning [notebook](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb) and [](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb) |
| 81 | + - Training model for cars segmentation on CamVid dataset [here](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/cars%20segmentation%20(camvid).ipynb). |
82 | 82 | - Training SMP model with [Catalyst](https://github.com/catalyst-team/catalyst) (high-level framework for PyTorch), [TTAch](https://github.com/qubvel/ttach) (TTA library for PyTorch) and [Albumentations](https://github.com/albu/albumentations) (fast image augmentation library) - [here](https://github.com/catalyst-team/catalyst/blob/v21.02rc0/examples/notebooks/segmentation-tutorial.ipynb) [](https://colab.research.google.com/github/catalyst-team/catalyst/blob/v21.02rc0/examples/notebooks/segmentation-tutorial.ipynb)
|
83 | 83 | - Training SMP model with [Pytorch-Lightning](https://pytorch-lightning.readthedocs.io) framework - [here](https://github.com/ternaus/cloths_segmentation) (clothes binary segmentation by [@ternaus](https://github.com/ternaus)).
|
84 | 84 |
|
@@ -465,7 +465,7 @@ $ pip install git+https://github.com/qubvel/segmentation_models.pytorch
|
465 | 465 | ### 🏆 Competitions won with the library
|
466 | 466 |
|
467 | 467 | `Segmentation Models` package is widely used in the image segmentation competitions.
|
468 |
| -[Here](https://github.com/qubvel/segmentation_models.pytorch/blob/master/HALLOFFAME.md) you can find competitions, names of the winners and links to their solutions. |
| 468 | +[Here](https://github.com/qubvel/segmentation_models.pytorch/blob/main/HALLOFFAME.md) you can find competitions, names of the winners and links to their solutions. |
469 | 469 |
|
470 | 470 | ### 🤝 Contributing
|
471 | 471 |
|
@@ -500,4 +500,4 @@ make table # generate table with encoders and print to stdout
|
500 | 500 | ```
|
501 | 501 |
|
502 | 502 | ### 🛡️ License <a name="license"></a>
|
503 |
| -Project is distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE) |
| 503 | +Project is distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/main/LICENSE) |
0 commit comments