3.6.0

Hey @channel
We're pleased to announce the release of Super-Gradients 3.6.0 🙂 .
This update includes several important changes and improvements:
Changes and Enhancements

• Added segmentation samples and support for albumentation transforms for segmentation, contributed by @shaydeci.
• Implemented distance-based detection matching in DetectionMetrics as an enhancement by @DimaBir.
• New training hyperparameter - finetune, and multiple LR assignment by @shaydeci, read about it here
• Enhanced ImagePermute processing inclusion, by @BloodAxe.
• Improved dataset plotting and plot functionality, by
  @Louis Dupont
  .
• Updated prediction notebooks and documentation, thanks to
  @Louis Dupont
  .
• Supported depth estimation dataset and added depth estimation metrics support, by @shaydeci.
• Proposed an API for checking model input compatibility, by @BloodAxe.
• Extended predict() support for segmentation models, by @BloodAxe.
• Removed deprecated features from version 3.6.0, by @shaydeci.
• Updated pre-trained models badge URL, contributed by @gasparitiago.
• Made changes to PPYoloELoss, removing the requirement for a reg_max parameter, by @BloodAxe.
• Switched to using onnxsim instead of onnx-simplifier for consistency in naming, thanks to @BloodAxe.
  Bugfixes
• Resolved a bug in OhemLoss thanks to @danielafrimi.
• Updated conditions to ensure functionality only on rank 0 where [context.sg](http://context.sg/)_logger is available, by @shaydeci.
• Modified the default set_device value to prevent unintentional launch of DDP, updated by
  @Louis Dupont
  .
• Addressed a bug where multigpu=None with device=cpu wasn't functioning as expected, thanks to
  @Louis Dupont
  .
• Adjusted bounding box thickness and text size relative to bbox size, for object detection model's predict() by
  @Louis Dupont
  .
• Addressed a bug in DetectionMixup that affected YoloXTrainingStageSwitchCallback, by @BloodAxe.
• Corrected a typo in an exception message variable name, by @BloodAxe.
• Reintegrated tests and refined CI/CD workflows, thanks to @shaydeci and @Yonatan-Kaplounov.
• Fixed import issues and improved model flexibility and metrics handling, mainly by @BloodAxe.
• Ensured class names in DetectionDataset are contained within a trivial container, by @BloodAxe.
• Fixed ExtremeBatchDetectionVisualizationCallback for multiscale collate function, by @BloodAxe.
• Several bug fixes and improvements in DistanceBasedDetectionMetrics and DetectionMetrics, by @BloodAxe.
  And various other fixes and improvements across the board to enhance functionality and user experience.
  For a detailed list of changes, refer to the full changelog.
  New Contributors
• Welcome to the contributors' team, @DimaBir, @gasparitiago, and @Yonatan-Kaplounov, for making their first contributions.Hey @channel
  We're pleased to announce the release of Super-Gradients 3.6.0 🙂 .
  This update includes several important changes and improvements:
  Changes and Enhancements
• Added segmentation samples and support for albumentation transforms for segmentation, contributed by @shaydeci.
• Implemented distance-based detection matching in DetectionMetrics as an enhancement by @DimaBir.
• New training hyperparameter - finetune, and multiple LR assignment by @shaydeci, read about it here
• Enhanced ImagePermute processing inclusion, by @BloodAxe.
• Improved dataset plotting and plot functionality, by
  @Louis Dupont
  .
• Updated prediction notebooks and documentation, thanks to
  @Louis Dupont
  .
• Supported depth estimation dataset and added depth estimation metrics support, by @shaydeci.
• Proposed an API for checking model input compatibility, by @BloodAxe.
• Extended predict() support for segmentation models, by @BloodAxe.
• Removed deprecated features from version 3.6.0, by @shaydeci.
• Updated pre-trained models badge URL, contributed by @gasparitiago.
• Made changes to PPYoloELoss, removing the requirement for a reg_max parameter, by @BloodAxe.
• Switched to using onnxsim instead of onnx-simplifier for consistency in naming, thanks to @BloodAxe.
  Bugfixes
• Resolved a bug in OhemLoss thanks to @danielafrimi.
• Updated conditions to ensure functionality only on rank 0 where [context.sg](http://context.sg/)_logger is available, by @shaydeci.
• Modified the default set_device value to prevent unintentional launch of DDP, updated by
  @Louis Dupont
  .
• Addressed a bug where multigpu=None with device=cpu wasn't functioning as expected, thanks to
  @Louis Dupont
  .
• Adjusted bounding box thickness and text size relative to bbox size, for object detection model's predict() by
  @Louis Dupont
  .
• Addressed a bug in DetectionMixup that affected YoloXTrainingStageSwitchCallback, by @BloodAxe.
• Corrected a typo in an exception message variable name, by @BloodAxe.
• Reintegrated tests and refined CI/CD workflows, thanks to @shaydeci and @Yonatan-Kaplounov.
• Fixed import issues and improved model flexibility and metrics handling, mainly by @BloodAxe.
• Ensured class names in DetectionDataset are contained within a trivial container, by @BloodAxe.
• Fixed ExtremeBatchDetectionVisualizationCallback for multiscale collate function, by @BloodAxe.
• Several bug fixes and improvements in DistanceBasedDetectionMetrics and DetectionMetrics, by @BloodAxe.
  And various other fixes and improvements across the board to enhance functionality and user experience.
  For a detailed list of changes, refer to the full changelog.
  New Contributors
• Welcome to the contributors' team, @DimaBir, @gasparitiago, and @Yonatan-Kaplounov, for making their first contributions..