super-gradients

Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.

APACHE-2.0 License

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super-gradients - https://github.com/Deci-AI/super-gradients/releases/tag/3.7.1 Latest Release

Published by deci-services 7 months ago

This GitHub Release was done automatically by CircleCI

super-gradients - https://github.com/Deci-AI/super-gradients/releases/tag/3.7.0

Published by deci-services 7 months ago

This GitHub Release was done automatically by CircleCI

super-gradients - 3.6.1

Published by deci-services 8 months ago

New Features

Deprecations

Improvements

Bugfixes

Other

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.6.0...3.6.1

super-gradients - 3.6.1

Published by shaydeci 8 months ago

What's Changed

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.6.0...3.6.1

super-gradients - 3.6.0

Published by deci-services 9 months ago

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..
super-gradients - 3.5.0

Published by deci-services 11 months ago

This GitHub Release was done automatically by CircleCI

New features

Documentation

Bugfixes

New Contributors

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.4.1...3.5.0

super-gradients -

Published by deci-services 12 months ago

Bugfixes

Enhancement:

New Contributors

super-gradients - 3.4.0

Published by deci-services 12 months ago

New features

Improvements

Bugfixes

Other

What's Changed

New Contributors

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.3.0...3.4.0

super-gradients - 3.3.1

Published by deci-services almost 1 year ago

Super Gradients 3.3.1

Improvements

Bugfixes

New Contributors

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.3.0...3.3.1

super-gradients - 3.3.0

Published by deci-services about 1 year ago

This GitHub Release was done automatically by CircleCI

What's Changed

New Contributors

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.1.3...3.3.0

super-gradients - 3.2.1

Published by deci-services about 1 year ago

3.2 1 - Minor bugfixes release

TLDR:

  • Improvements in docs đź“ś
  • A few fixes in export API without introducing breaking changes đź’Ş

What's Changed

New Contributors

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.1.3...3.2.1

super-gradients - 3.2.0

Published by deci-services about 1 year ago

This GitHub Release was done automatically by CircleCI

What's Changed

New Contributors

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.1.3...3.2.0

super-gradients - 3.1.3

Published by shaydeci over 1 year ago

New features
predict() support for pose estimation (PR: #1142) and classification (PR: #1220)
The possibility to log scalar values with explicit time units associated with it: log losses per step, use TimerCallback that measures and log batch time (PR: #1181)
Support torch compile (PR: #1172)
Class agnostic NMS (PR: #1232)
Allow to compute & log per-class mAP scores (PR: #1238)
Small features
Add batch_size option to predict (PR: #1273)
Allows to pass user-defined target of IoU thresholds without asking user to edit IoUThresholds enum in SG codebase (PR: #1223)
Add DetectionVerticalFlip for bounding box detection augmentation (PR: #1237)
Support the option for multiple ignored indices for segmentation metrics (PR: #1177)
Simpify torch schedulers integration (PR: #1230)
support single output in YoloX NMS (PR: #813)
Compute the best threshold for each class in an efficient manner without added loops (PR: #973)
Bug fix
Hotfix/alg 1470 drop boxes padding by (PR: #1107)
Fix evaluate from recipe by (PR: #1170)
fixed bug of whitespace by (PR: #1173)
Fix collections.Iterable -> typing.Iterable to fix crash in python 3.10 by @BloodAxe in #1178
Fix missing encoding (PR: #1185)
default quantization params set in qat from config (PR: #1192)
Fix mkdirs in checkpoint (PR: #1198)
Fix DEKR’s replace_head & improve repr for keypoints transforms (PR: #1227)
Getting rid of “module.” heritage (PR: #1184)
Fix LayerNorm have a bias parameter attribute but is not instance of torch primitive modules (PR: #1229)
moved exceptions from training (PR: #1260)
Added explicit casting of input in predict pipeline. (PR: #1281)
kd ema model unrwap model fix (PR: #1283)
New Contributors
@allankouidri made their first contribution in #1185
@djm93dev made their first contribution in #1199
@danielafrimi made their first contribution in #1220
@RanZilberstein made their first contribution in #1228
@itaylevy-deci made their first contribution in #973
@LukeAI made their first contribution in #1237
@jorgectf made their first contribution in #1240
@jacobmarks made their first contribution in #1278

super-gradients - 3.1.2

Published by deci-services over 1 year ago

What's Changed

New Contributors

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.1.1...3.1.2

super-gradients - https://github.com/Deci-AI/super-gradients/releases/tag/3.1.1

Published by deci-services over 1 year ago

This GitHub Release was done automatically by CircleCI

super-gradients - https://github.com/Deci-AI/super-gradients/releases/tag/3.1.0

Published by deci-services over 1 year ago

This GitHub Release was done automatically by CircleCI

super-gradients - 3.0.9

Published by deci-services over 1 year ago

Release notes 3.0.9
Features

  • Object Detection (Yolox, PPyoloE) predict function on images, videos, GIF, and folders paths. The predict function works out of the box, no need to define pre and post-processing. Both pre and post-processing are taken from the training. Examples here. PR: #815, #804, #807, #829, #845

  • Support yolov5 format detection dataset by for YoloX, PPyoloE https://github.com/Deci-AI/super-gradients/pull/847

  • Base recipes for PPYoloE and Yolox of RF100 dataset.

  • DetectionOutputAdapter tutorial. The DetectionOutputAdapter is a class that converts the output of a detection model into a user-appropriate format.

  • Segformer model and recipe

  • Predict function on images and videos - taking the pre+post processing from the training recipe.

  • QAT&PTQ notebooks

  • Added student_adapter feature to KDModule, which lets the user pass a manipulated version of the input to the student model. PR: #820

  • Introduce min_samples param to dataloader_params, which repeats the dataset in case its size is smaller than the value of this parameter. PR: #838

Bug fixes:

super-gradients - 3.0.8

Published by deci-services over 1 year ago

Bug fixes:
Fix the compatibility of ONNX conversion with torch 1.11+
Fix the docker publication
Fix Pre-Prediciton callback override bug
Fix dataloader multiprocessing (for Mac OS and Windows)
Fix strict_load not being passed in evaluate_from_recipe
Resolve many potential circular imports
Correct albumentations import log
Fix incorrect tensor type & device when entire batch has no targets
Fix documentation format in many files

Features:
New documentation for many features (EMA, AMP, QAT, Knowledge-Distillation, Dataloaders, Optimizer, Schedulers…)
Added Lion optimizer
Added registration for SG Logger (to allow custom-defined loggers to be used from YAML)
DDRNet 39 pre-trained segmentation model
Mapillary dataset
RoboFlow100 benchmark and all 100 datasets
QATTrainer for training with QAT
QAT from recipe script
PP-YoloE pre-trained models
Pose Estimation models loss functions
Added Auxiliary heads to Unet
Channel wise distillation
ONNX Simplifier on ONNX conversion
Unet and Loss function for binary segmentation
New Transforms: DetectionPadToSize, DetectionImagePermute
Allow activation factory type to take resolved type as inp
New strict_load mode: key_matching.

super-gradients - 3.0.7

Published by deci-services over 1 year ago

What's Changed

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.0.6...3.0.7

super-gradients - 3.0.6

Published by deci-services almost 2 years ago

What's Changed

Bugfixes:

Features:

New Contributors

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.0.5...3.0.6

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