SOTA easy to use PyTorch-based DL training library

Last update: Jan 03, 2023

Overview

Easily train or fine-tune SOTA computer vision models from one training repository.

SuperGradients

Introduction

Welcome to SuperGradients, a free open-source training library for PyTorch-based deep learning models. SuperGradients allows you to train models of any computer vision tasks or import pre-trained SOTA models, such as object detection, classification of images, and semantic segmentation for videos and images.

Whether you are a beginner or an expert it is likely that you already have your own training script, model, loss function implementation, etc., and thus you experienced with how difficult it is to develop a production ready deep learning model, the overhead of integrating with existing training tools with very different and stiff formats and conventions, how much effort it is to find a suitable architecture for your needs when every repo is focusing on just one task.

With SuperGradients you can:

Train models for any Computer Vision task or import production-ready pre-trained SOTA models (detection, segmentation, and classification - YOLOv5, DDRNet, EfficientNet, RegNet, ResNet, MobileNet, etc.)
Shorten the training process using tested and proven recipes & code examples
Easily configure your own or use plug&play training, dataset, and architecture parameters.
Save time and easily integrate it into your codebase.

Table of Content:

Getting Started
- Quick Start Notebook
- Walkthrough Notebook
Installation Methods
Computer Vision Models' Pretrained Checkpoints
Contributing
Citation
Community
License

Getting Started

Quick Start Notebook

Get started with our quick start notebook on Google Colab for a quick and easy start using free GPU hardware

SuperGradients Quick Start in Google Colab

Download notebook

View source on GitHub

SuperGradients Walkthrough Notebook

Learn more about SuperGradients training components with our walkthrough notebook on Google Colab for an easy to use tutorial using free GPU hardware

SuperGradients Walkthrough in Google Colab

Download notebook

View source on GitHub

Installation Methods

Prerequisites

General requirements:

Python 3.7, 3.8 or 3.9 installed.
torch>=1.9.0
- https://pytorch.org/get-started/locally/
The python packages that are specified in requirements.txt;

To train on nvidia GPUs:

Nvidia CUDA Toolkit >= 11.2
CuDNN >= 8.1.x
Nvidia Driver with CUDA >= 11.2 support (≥460.x)

Quick Installation of stable version

Not yet avilable in PyPi

  pip install super-gradients

That's it !

Installing from GitHub

pip install git+https://github.com/Deci-AI/[email protected]

Computer Vision Models' Pretrained Checkpoints

Pretrained Classification PyTorch Checkpoints

Model	Dataset	Resolution	Top-1	Top-5	Latency b1_T4	Throughout b1_T4
EfficientNet B0	ImageNet	224x224	77.62	93.49	1.16ms	862fps
RegNetY200	ImageNet	224x224	70.88	89.35	1.07ms	928.3fps
RegNetY400	ImageNet	224x224	74.74	91.46	1.22ms	816.5fps
RegNetY600	ImageNet	224x224	76.18	92.34	1.19ms	838.5fps
RegNetY800	ImageNet	224x224	77.07	93.26	1.18ms	841.4fps
ResNet18	ImageNet	224x224	70.6	89.64	0.599ms	1669fps
ResNet34	ImageNet	224x224	74.13	91.7	0.89ms	1123fps
ResNet50	ImageNet	224x224	76.3	93.0	0.94ms	1063fps
MobileNetV3_large-150 epochs	ImageNet	224x224	73.79	91.54	0.87ms	1149fps
MobileNetV3_large-300 epochs	ImageNet	224x224	74.52	91.92	0.87ms	1149fps
MobileNetV3_small	ImageNet	224x224	67.45	87.47	0.75ms	1333fps
MobileNetV2_w1	ImageNet	224x224	73.08	91.1	0.58ms	1724fps

NOTE: Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1

Pretrained Object Detection PyTorch Checkpoints

Model	Dataset	Resolution	mAP^val 0.5:0.95	Latency b1_T4	Throughout b64_T4
YOLOv5 nano	COCO	640x640	27.7	6.55ms	177.62fps
YOLOv5 small	COCO	640x640	37.3	7.13ms	159.44fps
YOLOv5 medium	COCO	640x640	45.2	8.95ms	121.78fps
YOLOv5 large	COCO	640x640	48.0	11.49ms	95.99fps

NOTE: Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1 (latency) and batch size 64 (througput)

Pretrained Semantic Segmentation PyTorch Checkpoints

Model	Dataset	Resolution	mIoU	Latency b1_T4	Throughout b64_T4
DDRNet23	Cityscapes	1024x2048	78.65	25.48ms	37.4fps
DDRNet23 slim	Cityscapes	1024x2048	76.6	22.24ms	45.7fps
ShelfNet_LW_34	COCO Segmentation (21 classes from PASCAL including background)	512x512	65.1	-	-

NOTE: Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1 (latency) and batch size 64 (througput)

Contributing

To learn about making a contribution to SuperGradients, please see our Contribution page.

Our awesome contributors:

Made with contrib.rocks.

Citation

If you use SuperGradients library or benchmark in your research, please cite SuperGradients deep learning training library.

Community

If you want to be a part of SuperGradients growing community, hear about all the exciting news and updates, need help, request for advanced features, or want to file a bug or issue report, we would love to welcome you aboard!

Slack is the place to be and ask questions about SuperGradients and get support. Click here to join our Slack
To report a bug, file an issue on GitHub.
You can also join the community mailing list to ask questions about the project and receive announcements.

License

This project is released under the Apache 2.0 license.

Comments

Add a `conda` install option for `super-gradients`
It will be helpful to have super-gradients added to conda-forge channel. I have started the work already in the following PR.

https://github.com/conda-forge/staged-recipes/pull/20167

But there seems to be a problem with one of its dependencies:

deci-lab-client PyPI

Neither does it have any source file (*.tar.gz) on PyPI, nor any release on a public GitHub repository.

Please provide (preferably) the source file for deci-lab-client on PyPI.

:fire: CONSTRAINT: To add any package on conda-forge channel, you need ALL its dependencies on conda-forge as well
opened by sugatoray 9
Add ignore option for dataloder workers on windows

I added a decorator for making sure a function does not run on dataloder workers. This is needed to protect functions called when importing super_gradients on windows, because it seems that dataloader workers on windows re-import all the packages (see https://pytorch.org/docs/stable/data.html#multi-process-data-loading)

Especially:

Still need to be tested on windows
size/S

opened by Louis-Dupont 5
interact with the platform through the lab client SDK
This only addresses the immediate fix.

This is still not ideal. From this small fix it felt not intuitive to start building the mode name by myself. What if the #train returned a training context - one that returns data about the process that just ran. Something like

class ResultsContext(Enum): TRAINNING = 'training' CONVERSION = 'conversion' LAB_UPLOAD = 'lab_upload' class SgModel: ... def train(self, training_params: dict = dict()): -> Dict[ResultsContext, dict]

That way, I'd just have to access the corresponding context in order to get the model name that the server returned....otherwise, what happens when tomorrow the server changes its logic and _1_1 suffixes won't be a thing anymore?
size/M
opened by daniel-deci 5
Crash tip

When starting we get this message:

ERRORS:

Wrong format when creating a object with factory:

DDP not setup:

Relies on https://github.com/Deci-AI/super-gradients/pull/496 (We might want to handle exceptions from within this decorator)
include in feature update

opened by Louis-Dupont 4
There seems to be a problem with the MobilNetV3 structure

Hello, the network structure of MobileNetV3 seems to be different from that of the original author. The SE module of V3 is not a module that uses SENet. The author changed nn.Line to nn.Conv
bug

opened by Daming-TF 4
CoCoSegmentationDataSet._generate_samples_and_targets() doesn't call super (= no caching)

Describe the bug

CoCoSegmentationDataSet._generate_samples_and_targets() doesn't call the corresponding parent class method, and therefore image and label caching is doesn't work for this class. The solution is to add super()._generate_samples_and_targets() as the last line in CoCoSegmentationDataSet._generate_samples_and_targets(). Excuse me for not making this as a pull request this time.
bug

opened by michaelitvin 3
Feature/SG-245 Support for register model in model's factory
Added @register decorator to easily map models to the global ARCHITECTURES variable. Input your desired architecture in the config via the function name that creates it, for example, architecture: ResNet18Cifar

This is a draft commit. Please confirm that you like the solution. If accepted, the next step would be to manually add @register decorators to all models to get back to feature-parity.

There is no elegant way to support an in-between step of supporting both (register + dict) methods.

size/L
opened by danbochman 3
Hydra outputs moved to checkpoints/experiment_name dir
Change behaviour of Hydra to output logs to user's checkpoints/experiment_name directory Current behaviour spams output directories with unique timestamps for every run

This solves the bug where datasets such as cifar10, mnist, etc. were downloaded and extracted from scratch in each run. In addition, this allows for easy loading of trained model, since now checkpoints are stored together with the YAML config that created them. A simple script can rebuild the model and load the weighs to it.

flake8 cleanup

environment_config.py: line 6 added package checkpoints dir as environment variable

env_helpers.py: line 60 added argument for Hydra output dir init

checkpoint_utils: line 51 used global PKG_CHECKPOINTS_DIR since it already exists

I recommend removing support for "external_checkpoint_path"; users can create symbolic links instead for now
size/L
opened by danbochman 3
YOLOv5 Tutorial Assistance
@shaydeci @oferbaratz @ofrimasad hi I'm working on our new Deci.ai + YOLOv5 partnership tutorial and need some help. The tutorial is at https://github.com/ultralytics/yolov5/wiki/YOLOv5-Deci-AI-Tutorial and is based on a word document provided by Rachel than I've converted to Markdown.

I'd like you guys at Deci to review the content and help supply the 6 additional images (denoted by IMAGE_HERE). To streamline this I've pasted the markdown content directly here in this issue. If you simply edit this issue with the appropriate changes I can then transfer those over to the YOLOv5 repo.

For the images I've provided one example hyperlinked image myself. The guidelines are that they should be 1920 pixel wide JPG screenshots at <500kB each.

Thanks for the help and let me know if you have any questions!

Markdown content below

📚 This guide explains how to streamline the process of compiling and quantizing YOLOv5 🚀 to achieve better performance with the Deci platform. UPDATED 6 August 2022.

Content

About the Deci Platform

First-time setup

Runtime optimization and benchmarking of your model

About Deci Platform

The Deci platform includes free tools for easily managing, optimizing, and deploying models in any production environment. Deci supports all popular DL frameworks, such as TensorFlow, PyTorch, Keras and ONNX. All you need is our web-based platform or our Python client to run it from your code.

With Deci you can:

Improve Inference performance by up to 10X
Automatically compile and quantize your models and evaluate different production settings to achieve better latency, throughout, reduce model size and memory footprint on your hardware.

Find the best inference hardware for your application
Benchmark your model's performance on various hardware (including edge) devices with a click of a button. Eliminate the need to manually setup and test various hardware and production settings.

Deploy with a Few Lines of Code
Leverage Deci's python-based inference engine. Compatible with multiple frameworks and hardware types.

For more information about the Deci platform please visit Deci's website.

First-time setup

Step 1:

Go to https://console.deci.ai/sign-up and open your free account.

Step 2:

In order to start optimizing your pre-trained YOLOv5 model, you will need to convert it into an ONNX format. Please follow these simple instructions on this link to convert your model to ONNX format.

Step 3:

Go to "Lab" tab and click the "New Model" button in the top right part of the screen to upload your model in the ONNX format.

Follow the steps of the model upload wizard to select your target hardware as well as desired batch size and quantization level for the model compilation.

After filling in the relevant information, click "Start". The Deci platform will automatically perform a runtime optimization of your YOLOv5 model for the hardware you selected as well as benchmark your model on various hardware types. This process takes approximately 10 minutes.

Once done, a new row will appear on your screen underneath the baseline model you previously uploaded. Here you can see the optimized version of your pre-trained YOLOv5 model.

What's next?

You can then download your optimized model by clicking on "Deploy" button

You will then be prompted to download your model and receive the instructions on how to install and use Infery - Deci's runtime inference engine.

The use of Infery is optional. You can get the python raw files and use them with any other inference engine of your choice.

Explore the optimization and benchmark results on the "Insights" tab.

help wanted
opened by glenn-jocher 3
Add DiceCEEdge loss recipe
DiceEdge recipe results

New recipe for STDC training, using Edge attention loss and Dice loss, replacing the not stable detail loss. Results: | model | mIoU | previous %mIoU diff | | ------ | ----- | --------------- | | STDC1Seg50 | 75.11 | 0.75 | | STDC2Seg50 | 76.44 | 1.17 |

This PR includes:

Target / one-hot to binary edge map util functions:

create edge feature maps function from one hot tensor. using dilation erosion with convolution.

Edge widths can be adjusted with kernel_size argument, typical edge width is kernel_size -1

input can be a multi-class or one class one hot tensor with shape [B, C, H, W]

In case of multi-class the result tensor can be flattened to one channel edge map, or keep the channel dimension so each channel is the edge features per class. see below example.

Only odd kernel_size are valid to prevent dimension change or pixel shifting.

Cityscapes example:

kernel_size: left k=3, right k=9.

Flatten_channels = False: left: person class edges, right: car class edges

Mask Loss (edge loss):

Mask attention loss, for region enforced loss functions i.e edge loss.

support many criterion functions, such as, CrossEntropyLoss, BCEWithLogitsLoss, MSELoss, SL1Loss.

Losses is composed of regular loss and mask loss, which are weighted with the loss_weights argument.

unit tests added.

DiceCEEdgeLoss main class and recipe:

DiceCEEdgeLoss: main loss class for losses combination of dice, edge-ce and auxiliaries.

yaml only recipe

size/XL
opened by lkdci 3
How to accelerate regseg on tensorRT

I tried to use tensorrt with original regseg repository. However onnx had trouble with torch.split and also torch2trt unable to use with specific tensorrt version. Please let me know how did you use tensorrt with regseg model when measure the latency.

opened by odyssey0529 3
Easy support of custom datasets
Problem to solve:

Our transforms expect a Dict, with specific fields ("image", "target", ...)

We don't know the output format of custom dataset.

Solution:

Wrapping the original dataset to take the output of the original dataset and build a sample dict.

Giving the option to provide "adapter" functions that takes the output of the user dataset and adapts it to fit the input format of our transforms
opened by Louis-Dupont 1
Integration with 🤗 Hub

Hi folks.

Thanks for providing the pre-trained models along with pre-training scripts.

At Hugging Face, the Hub is our house to serve models, datasets, spaces, etc. It facilitates easy artifact loading and usage, providing common and streamlined API access to your models, datasets, etc.

Hugging Face supports third-party integrations too and I was wondering if you'd be up to exploring the integration. The integration will primarily facilitate easy model sharing and model downloading which could be beneficial for the vision community in general.

Here's the main doc that'd be helpful for you for the integration: https://huggingface.co/docs/hub/models-adding-libraries. Let me know if you'd need any help.

opened by sayakpaul 1
Refactored scheduler callbacks (epoch-based/step-based warmup)
Changes:

warmup_mode: linear_step will emit deprecation warning. But will continue to work without any changes.

Two new modes with explicit meaning:

warmup_mode: linear_batch_step

warmup_mode: linear_epoch_step

Per-batch warmup supports two modes:

Fixed number of warmup steps (batches):

warmup_mode: linear_batch_step num_warmup_steps: 100

Warmup across N epochs (LR still updated on each batch)

warmup_mode: linear_batch_step num_warmup_epochs: 4
opened by BloodAxe 1

Releases(3.0.5)

3.0.5(Dec 28, 2022)
What's Changed

Added warning for drop_last when train_loader is not divisible by batch_size by @shaydeci in https://github.com/Deci-AI/super-gradients/pull/586

fix compute_detection_metrics_per_cls return value when no detection … by @ofrimasad in https://github.com/Deci-AI/super-gradients/pull/583

Quantization infra mods for different calibrators and learnable amax by @spsancti in https://github.com/Deci-AI/super-gradients/pull/537

PLFM-3331 Register experiments with model name by @roikoren755 in https://github.com/Deci-AI/super-gradients/pull/585

fix ignore index for DiceCEEdgeLoss by @lkdci in https://github.com/Deci-AI/super-gradients/pull/588

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.0.4...3.0.5
Source code(tar.gz)
Source code(zip)
3.0.4(Dec 21, 2022)
What's Changed

Reorganisation README by @Shani-Perl in https://github.com/Deci-AI/super-gradients/pull/526

Apply black formatting on pretrained model zoo by @BloodAxe in https://github.com/Deci-AI/super-gradients/pull/524

Remove imports from factory.init by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/516

Feature/sg 416 albumentations plugin for classification by @shaydeci in https://github.com/Deci-AI/super-gradients/pull/495

Feature/sg 404 ssd reproduce by @BloodAxe in https://github.com/Deci-AI/super-gradients/pull/525

Added indexing support for meshgrid by @BloodAxe in https://github.com/Deci-AI/super-gradients/pull/528

Feature/al 441 ptq detection by @spsancti in https://github.com/Deci-AI/super-gradients/pull/527

Add DDP doc by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/523

SG-448- dataset_params and arch_params logging by @shaydeci in https://github.com/Deci-AI/super-gradients/pull/532

Fix clearml comment by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/530

Bug/sg 000 torch version fix by @shaydeci in https://github.com/Deci-AI/super-gradients/pull/535

fix pplite-seg prep conversion by @lkdci in https://github.com/Deci-AI/super-gradients/pull/538

SGLogger fix - add warning and multiprocess_safe system_monitoring by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/534

Add self to recipes by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/539

replace head for UNet module by @lkdci in https://github.com/Deci-AI/super-gradients/pull/543

Rename gpu_mode to multi_gpu and setup_gpu_mode to setup_device by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/517

Add version_base to hydra to remove warning by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/541

Hotfix/sg 000 add data structure and link to cityscape desc by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/550

add stringcase by @ofrimasad in https://github.com/Deci-AI/super-gradients/pull/553

Hotfix/sg 000 add data structure to cityscape desc v2 by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/551

Improve PascalVOC detection error msg by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/556

Improve evaluate_from_recipe usability by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/558

Feature/sg 458 support for any user dataloader using dataset registry by @shaydeci in https://github.com/Deci-AI/super-gradients/pull/547

Features/sg 409 check all params used by @BloodAxe in https://github.com/Deci-AI/super-gradients/pull/546

Split and rename the modules from super_gradients.common.environment by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/548

Feature/sg 468 detection transform support for any number of channels by @shaydeci in https://github.com/Deci-AI/super-gradients/pull/559

Feature/sg 518 update evaluate from recipe to add output by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/560

error raised for torch version and formatting by @shaydeci in https://github.com/Deci-AI/super-gradients/pull/540

Feature/sg 356 ddp silent mode and multi process safe docs by @shaydeci in https://github.com/Deci-AI/super-gradients/pull/563

Apply black on some files by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/565

Fix runtime warning of accessing non-contiguous tensor in mAP metric by @BloodAxe in https://github.com/Deci-AI/super-gradients/pull/566

fix F1 Precision Recall to represent all the defined rage and not onl… by @ofrimasad in https://github.com/Deci-AI/super-gradients/pull/564

Fix Multigpu.OFF factory by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/569

Import AutoLoggerConfig and ConsoleSink separatly by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/567

remove user guide from docs by @ofrimasad in https://github.com/Deci-AI/super-gradients/pull/570

new generated docs by @ofrimasad in https://github.com/Deci-AI/super-gradients/pull/572

update documentation by @ofrimasad in https://github.com/Deci-AI/super-gradients/pull/573

Make ConvBNReLU a subclass of ConvBNAct to keep backward compatibililty by @BloodAxe in https://github.com/Deci-AI/super-gradients/pull/554

Update README.md by @ofrimasad in https://github.com/Deci-AI/super-gradients/pull/575

Apply black on losses by @Louis-Dupont in https://github.com/Deci-AI/super-gradients/pull/576

Feature/sg 516 support head replacement for local pretrained weights unknown dataset by @shaydeci in https://github.com/Deci-AI/super-gradients/pull/578

Fix bug of not respecting dilation argument in RepVGGBlock by @BloodAxe in https://github.com/Deci-AI/super-gradients/pull/555

Feature/sg 431 check classes by @BloodAxe in https://github.com/Deci-AI/super-gradients/pull/531

register unet cls module by @lkdci in https://github.com/Deci-AI/super-gradients/pull/580

feature: Accept an arbitrary WandB ID by @yurkovak in https://github.com/Deci-AI/super-gradients/pull/582

Bug/sg 512 shuffle bugfix in recipe datalaoders by @shaydeci in https://github.com/Deci-AI/super-gradients/pull/581

Added import of quantized modules so they get registered by @spsancti in https://github.com/Deci-AI/super-gradients/pull/542

Use unpack_batch_items on first batch (Respect additional batch items) by @BloodAxe in https://github.com/Deci-AI/super-gradients/pull/584

Full Changelog: https://github.com/Deci-AI/super-gradients/compare/3.0.3...3.0.4
Source code(tar.gz)
Source code(zip)
3.0.3(Nov 28, 2022)
ClearML integration.

Console logging + upload (Lab visibility).

Epoch summary (visual improvement).

Registry for Phase Callbacks and Transforms.

Infrastructure for crash tips.

Detection output adapter.

Train from recipe example- external dataset.

QAT infrastructure + examples.

System Logger (i.e device attributes etc).

Source code(tar.gz)
Source code(zip)
3.0.2(Nov 13, 2022)
dataloaders.get() supports Dataset objects.

Behind the scenes sampler handling (warning + initialization).

Black formatter.

New DetectionBase.

Logs moved from ~/sg_log/{module_name}

CSP Resnet Backbone, new place for modules in repo.

Plenty new factories.

Message on effective batch size

External checkpoints resume- fix

Passing external classes list for CocoDetection

Fix for loading weights from the platform

Source code(tar.gz)
Source code(zip)
3.0.1(Oct 11, 2022)
What's new?

Eval recipe- perform validation by recipe name.

Supported strings class for convenient autocomplete in IDE's.

Registry: metrics, dataloaders, models, losses (so they can be passed as strings when using train_from_config).

Return model, results from train

Load backbone fix https://github.com/Deci-AI/super-gradients/pull/408/files

PPLiteSeg training recipes.

AWS env check removal

New resolvers support: "+" "if" for yaml recipes (i.e Hydra resolvers).

Support for custom STDC.

ShelfNet "classes_num" -> "num_classes" bug fix.

Improve cross-platform compatibility when parsing a readme description.

Added the ability to download and import external code for models from ADK.

Source code(tar.gz)
Source code(zip)
3.0.0(Sep 20, 2022)
DatasetInterface class removal- refactored as torch.DataLoader objects configured by src/training/recipes, using super_gradients.dataloaders.get() (see new updated tutorials and snippets).

Trainer.build_model() removal- models initialisation refactored with super_gradients.models.get() (see updated tutorials and notebooks).

Coded DDP launch (no need for python -m torch.distributed.launch ...), see new snippets here .

Updated notebooks, tutorials and code snippets in readme.md.

Extract recipes training hyper_params config with super_gradients.training_hyperparams.get() (see updated tutorials and notebooks).

Simplfied resume- now passed through train_params in SgTrainer.train() (see updated snippets in readme.md).

Removal of "loss_loggging_items_names" from train_params in Trainer.train().

Trainer.init old, unnecessary args removed.

Add support for getting models from Deci's platform using super_gradients.models.get(), more info regarding Deci's platform in readme.md.

Source code(tar.gz)
Source code(zip)
2.6.0(Sep 12, 2022)

This GitHub Release was done automatically by CircleCI
Source code(tar.gz)
Source code(zip)
2.5.0(Sep 12, 2022)

This GitHub Release was done automatically by CircleCI
Source code(tar.gz)
Source code(zip)
2.2.0(Aug 14, 2022)

This GitHub Release was done automatically by CircleCI
Source code(tar.gz)
Source code(zip)
2.1.0(Jul 19, 2022)
YoloX architectures.

SSDLite Mobilenet V2 COCO recipe

QAT support with Nvidia's pytorch-quantization (optional dependency).

COCO mAP calculation support in DDP (torch metric object, supports "crowd" labels).

Pre_prediction_callback- support for input and targets manipulation right before forward pass + multiscaling pre_prediction_callbacks that work out of the box in DDP (classification and Object detection).

Training stage switch callback to support multi-stage training.

Source code(tar.gz)
Source code(zip)
2.0.1(Jun 12, 2022)

This GitHub Release was done automatically by CircleCI
Source code(tar.gz)
Source code(zip)
2.0.0(Jun 9, 2022)
Features:

KD trainer and resnet50 recipe (81.92 % accuracy)

Repeated augmentation sampler

Cooldown epochs

Beit architecture

Lamb Optimizer

Passing torch data loaders directly to SgModel.

Refactoring:

Checkpoint and architecture params decoupled.

Source code(tar.gz)
Source code(zip)
1.7.5(Apr 19, 2022)
STDC architectures binary segmentation support bug fix.

Source code(tar.gz)
Source code(zip)
1.7.4(Apr 17, 2022)

torchmetrics version requirement set to 0.7.3 to avoid metric names depreciation bug.
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1.7.3(Apr 6, 2022)

This GitHub Release was done automatically by CircleCI
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1.7.2(Apr 5, 2022)

This GitHub Release was done automatically by CircleCI
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Source code(zip)
1.7.1(Mar 10, 2022)
What's new ?

BCE with Dice loss.

Binary IOU metric object (I.e IOU only for target class).

Binary segmentation visualisation callback.

Supervisely dataset interface.

Different lr assignment for head and backbone for RegSeg.

Google Colab notebook for semantic segmentation quick start - Check it out in our GitHub repo README.md

Google Colab notebook for semantic segmentation transfer learning - Check it out in our GitHub repo README.md

Source code(tar.gz)
Source code(zip)
1.7.0(Mar 10, 2022)

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1.6.0(Feb 8, 2022)
Added RegSeg model, recipe, and pre-trained checkpoints.

Updated EfficientNet recipe.

Updated Resnet50 recipe + pre-trained checkpoint (Top-1=79.47)

Source code(tar.gz)
Source code(zip)
1.5.2(Jan 26, 2022)
Detection visualisation callback, wrong color ordering for images in tensor board fix.

Source code(tar.gz)
Source code(zip)
1.5.1(Jan 25, 2022)
Minor fixes for transfer learning example notebook support.

Source code(tar.gz)
Source code(zip)
1.5.0(Jan 20, 2022)
What’s new?

STDC family - new recipes added with even higher mIoU:muscle:

Google Colab notebook for transfer learning / fine-tuning (COCO pre-trained YOLOv5 nano into PASCAL VOC sub dataset) - Check it out in our GitHub repo README.md

Factories for yaml string interpolation

Source code(tar.gz)
Source code(zip)
1.4.0(Jan 16, 2022)
Transfer learning example added for YoloV5.

Optional checkpoint root directory path argument added to SgModel.

Regnet recipes updated.

Source code(tar.gz)
Source code(zip)
1.3.1(Jan 6, 2022)

Checkpoints root directory fix- allowing users to explicitly define checkpoints root directory instead of depriving it from content root.
Source code(tar.gz)
Source code(zip)
1.3.0(Jan 5, 2022)
Upload to pipy added to ci workflow.

Max epochs limitation removed.

Post training phase moved before closing loggers

Source code(tar.gz)
Source code(zip)
1.2.0(Jan 3, 2022)
Added an option to upload any file from the checkpoint dir.

Added a new callback phase and calling it on best metric.

Automatic GPU mode for training recipes.

Readme updates.

Sphinx documentation updates.

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1.1.0(Dec 30, 2021)

This GitHub Release was done automatically by CircleCI
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1.0.1(Dec 26, 2021)

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1.0.0(Dec 23, 2021)

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0.1.0(Dec 22, 2021)

-Pretrained classification models with head replacement -Quickstart notebook added -Optionally loading EMA net for testing
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Owner

GitHub Repository

SOTA easy to use PyTorch-based DL training library

Related tags

Overview

SuperGradients

Introduction

Table of Content:

Getting Started

Quick Start Notebook

SuperGradients Walkthrough Notebook

Installation Methods

Prerequisites

Quick Installation of stable version

Installing from GitHub

Computer Vision Models' Pretrained Checkpoints

Pretrained Classification PyTorch Checkpoints

Pretrained Object Detection PyTorch Checkpoints

Pretrained Semantic Segmentation PyTorch Checkpoints

Contributing

Citation

Community

License

Comments

Describe the bug

Markdown content below

Content

About Deci Platform

With Deci you can:

First-time setup

Step 1:

Step 2:

Step 3:

What's next?

DiceEdge recipe results

Target / one-hot to binary edge map util functions:

Mask Loss (edge loss):

DiceCEEdgeLoss main class and recipe:

Changes:

Per-batch warmup supports two modes:

Releases(3.0.5)

3.0.5(Dec 28, 2022)

What's Changed

3.0.4(Dec 21, 2022)

What's Changed

3.0.3(Nov 28, 2022)

3.0.2(Nov 13, 2022)

3.0.1(Oct 11, 2022)

3.0.0(Sep 20, 2022)

2.6.0(Sep 12, 2022)

2.5.0(Sep 12, 2022)

2.2.0(Aug 14, 2022)

2.1.0(Jul 19, 2022)

2.0.1(Jun 12, 2022)

2.0.0(Jun 9, 2022)

1.7.5(Apr 19, 2022)

1.7.4(Apr 17, 2022)

1.7.3(Apr 6, 2022)

1.7.2(Apr 5, 2022)

1.7.1(Mar 10, 2022)

1.7.0(Mar 10, 2022)

1.6.0(Feb 8, 2022)

1.5.2(Jan 26, 2022)

1.5.1(Jan 25, 2022)

1.5.0(Jan 20, 2022)

1.4.0(Jan 16, 2022)

1.3.1(Jan 6, 2022)

1.3.0(Jan 5, 2022)

1.2.0(Jan 3, 2022)

1.1.0(Dec 30, 2021)

1.0.1(Dec 26, 2021)

1.0.0(Dec 23, 2021)

0.1.0(Dec 22, 2021)

Owner

Pytorch implementation of MaskFlownet

Learning Synthetic Environments and Reward Networks for Reinforcement Learning

Super Resolution for images using deep learning.

Lightweight, Python library for fast and reproducible experimentation :microscope:

This code is an unofficial implementation of HiFiSinger.

Pytorch Lightning Distributed Accelerators using Ray

Semi-SDP Semi-supervised parser for semantic dependency parsing.

GLIP: Grounded Language-Image Pre-training