Add features to the YOLO model from the latest YOLO variants #817
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* Supports several algorithms for matching targets to anchors. * Added support for DIoU and CIoU losses. * Target for confidence can be selected between 1.0 and the overlap between the target and the predicted box. * Target classes may be specified as a matrix of class probabilities, allowing multiple classes per object. * Fixed rounding of maxpool layer size. * Weight decay is applied only to convolutional layer weights. * Command line interface is now using LightningCLI.
* Calculates MAP metric using TorchMetrics. * Convolutional and max pooling layers automatically add the correct amount of padding. * Added YOLOv4 network.
Loading a checkpoint with a different number of output classes is possible with strict=False.
…ities.rank_zero is not available
* Added the deeper P6 variant of YOLOv4. * Width of each backbone stage can be configured freely. * The number of input channels in the PyTorch architectures can be changed. * Added predict_step().
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@senarvi we have fixed master a few munites ago, so shall be 🟢 could you pls merge it here 🐿️ |
Great. Seems like the tests are passing now. |
perfect, it just sai about some conflicts, but I can't see them, mind check it? |
Head branch was pushed to by a user without write access
mypy doesn't understand "= ... if ... else ..." and ruff doesn't accept the longer form.
@Borda managed to fix it finally. ruff formatted the code in a way that caused mypy to fail. Formatting in a way that mypy accepts caused ruff to fail. In the end I added a "type: ignore" comment. It wasn't merged automatically, but all the tests have passed. |
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| # YOLO | |||
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| The YOLO model has evolved quite a bit, since the original publication in 2016. The original source code was written in C, using a framework called [Darknet](https://github.com/pjreddie/darknet). The final revision by the original author was called YOLOv3 and described in an [arXiv paper](https://arxiv.org/abs/1804.02767). Later various other authors have written implementations that improve different aspects of the model or the training procedure. [YOLOv4 implementation](https://github.com/AlexeyAB/darknet) was still based on Darknet and [YOLOv5](https://github.com/ultralytics/yolov5) was written using PyTorch. Most other implementations are based on these. | |||
What does this PR do?
The YOLO model has been largely refactored, so that it's easy to incorporate new features from different YOLO variants, such as different algorithms for assigning targets to anchors. It also supports defining the network structure as a PyTorch module, in addition to loading a Darknet configuration file. The implementation now supports the most important features of YOLOv3, YOLOv4, YOLOv5, YOLOv7, Scaled-YOLOv4, and YOLOX.
Fixes #816
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