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WebsiteWhy Use SG?User GuideDocsGetting Started NotebooksTransfer LearningPretrained ModelsCommunityLicenseDeci Platform

# SuperGradients ## Introduction Welcome to SuperGradients, a free, open-source training library for PyTorch-based deep learning models. SuperGradients allows you to train or fine-tune SOTA pre-trained models for all the most commonly applied computer vision tasks with just one training library. We currently support object detection, image classification and semantic segmentation for videos and images. Docs and full user guide[](#) ### Why use SuperGradients? **Built-in SOTA Models** Easily load and fine-tune production-ready, [pre-trained SOTA models](https://github.com/Deci-AI/super-gradients#pretrained-classification-pytorch-checkpoints) that incorporate best practices and validated hyper-parameters for achieving best-in-class accuracy. **Easily Reproduce our Results** Why do all the grind work, if we already did it for you? leverage tested and proven [recipes](https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/recipes) & [code examples](https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples) for a wide range of computer vision models generated by our team of deep learning experts. Easily configure your own or use plug & play hyperparameters for training, dataset, and architecture. **Production Readiness and Ease of Integration** All SuperGradients models’ are production ready in the sense that they are compatible with deployment tools such as TensorRT (Nvidia) and OpenVINO (Intel) and can be easily taken into production. With a few lines of code you can easily integrate the models into your codebase.
## What's New * 【07/08/2022】DDRNet23 - new pre-trained [checkpoints](https://github.com/Deci-AI/super-gradients#pretrained-semantic-segmentation-pytorch-checkpoints) and [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes) for Cityscapes with SOTA mIoU scores (~1% above paper)🎯 * 【27/07/2022】YOLOX models (object detection) - recipes and pre-trained checkpoints. * 【07/07/2022】SSD Lite MobileNet V2,V1 - Training [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes/coco_ssd_lite_mobilenet_v2.yaml) and pre-trained [checkpoints](https://github.com/Deci-AI/super-gradients#pretrained-object-detection-pytorch-checkpoints) on COCO - Tailored for edge devices! 📱 * 【07/07/2022】 STDC - new pre-trained [checkpoints](https://github.com/Deci-AI/super-gradients#pretrained-semantic-segmentation-pytorch-checkpoints) and [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes) for Cityscapes with super SOTA mIoU scores (~2.5% above paper)🎯 * 【16/06/2022】 ResNet50 - new pre-trained checkpoint and [recipe](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes/imagenet_resnet50_kd.yaml) for ImageNet top-1 score of 81.9 💪 * 【09/06/2022】 ViT models (Vision Transformer) - Training [recipes](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes) and pre-trained [checkpoints](https://github.com/Deci-AI/super-gradients#pretrained-object-detection-pytorch-checkpoints) (ViT, BEiT). * 【09/06/2022】 Knowledge Distillation support - [training module](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/kd_model/kd_model.py) and [notebook](https://bit.ly/3HQvbsg). * 【06/04/2022】 Integration with professional tools - [Weights and Biases](https://bit.ly/3BJzCUv) and [DagsHub](https://bit.ly/3bznLhc). * 【09/03/2022】 New [quick start](#quick-start-notebook---semantic-segmentation) and [transfer learning](#transfer-learning-with-sg-notebook---semantic-segmentation) example notebooks for Semantic Segmentation. * 【07/02/2022】 We added RegSeg recipes and pre-trained models to our [Semantic Segmentation models](#pretrained-semantic-segmentation-pytorch-checkpoints). * 【01/02/2022】 We added issue templates for feature requests and bug reporting. * 【20/01/2022】 STDC family - new recipes added with even higher mIoU💪 Check out SG full [release notes](https://github.com/Deci-AI/super-gradients/releases). ## Coming soon - [ ] Single class detectors (recipes, pre-trained checkpoints) for edge devices deployment. - [ ] Single class segmentation (recipes, pre-trained checkpoints) for edge devices deployment. - [ ] QAT capabilities (Quantization Aware Training). - [ ] Dali implementation. - [ ] Integration with more professional tools. - [ ] Improved pre-trained checkpoints and recipes (DDRNet, ResNet, RegSeg, etc.) __________________________________________________________________________________________________________ ### Table of Content - [Getting Started](#getting-started) - [Quick Start Notebook - Classification example](#quick-start-notebook---classification) - [Quick Start Notebook - Semantic segmentation example](#quick-start-notebook---semantic-segmentation) - [Transfer Learning](#transfer-learning) - [Transfer Learning with SG Notebook - Semantic segmentation example](#transfer-learning-with-sg-notebook---semantic-segmentation) - [Knowledge Distillation Training](#knowledge-distillation-training) - [Knowledge Distillation Training Quick Start with SG Notebook - ResNet18 example](#knowledge-distillation-training-quick-start-with-sg-notebook---resnet18-example) - [Installation Methods](#installation-methods) - [Prerequisites](#prerequisites) - [Quick Installation](#quick-installation) - [Computer Vision Models - Pretrained Checkpoints](#computer-vision-models---pretrained-checkpoints) - [Pretrained Classification PyTorch Checkpoints](#pretrained-classification-pytorch-checkpoints) - [Pretrained Object Detection PyTorch Checkpoints](#pretrained-object-detection-pytorch-checkpoints) - [Pretrained Semantic Segmentation PyTorch Checkpoints](#pretrained-semantic-segmentation-pytorch-checkpoints) - [Implemented Model Architectures](#implemented-model-architectures) - [Contributing](#contributing) - [Citation](#citation) - [Community](#community) - [License](#license) - [Deci Platform](#deci-platform) ## Getting Started ### Start Training with Just 1 Command Line The most simple and straightforward way to start training SOTA performance models with SuperGradients reproducible recipes. Just define your dataset path and where you want your checkpoints to be saved and you are good to go from your terminal! ```bash python -m super_gradients.train_from_recipe --config-name=imagenet_regnetY architecture=regnetY800 dataset_interface.data_dir= ckpt_root_dir= ``` ### Quickly Load Pre-Trained Weights for Your Desired Model with SOTA Performance Want to try our pre-trained models on your machine? Import SuperGradients, initialize your SgModel, and load your desired architecture and pre-trained weights from our [SOTA model zoo](#computer-vision-models---pretrained-checkpoints) ```python # The pretrained_weights argument will load a pre-trained architecture on the provided dataset # This is an example of loading COCO-2017 pre-trained weights for a YOLOX Nano object detection model import super_gradients from super_gradients.training import SgModel trainer = SgModel(experiment_name="yoloxn_coco_experiment",ckpt_root_dir=) trainer.build_model(architecture="yolox_n", arch_params={"pretrained_weights": "coco", num_classes": 80}) ``` ### Quick Start Notebook - Classification Get started with our quick start notebook for image classification tasks on Google Colab for a quick and easy start using free GPU hardware.
Classification Quick Start in Google Colab Download notebook View source on GitHub


### Quick Start Notebook - Semantic Segmentation Get started with our quick start notebook for semantic segmentation tasks on Google Colab for a quick and easy start using free GPU hardware.
Segmentation Quick Start in Google Colab Download notebook View source on GitHub


## Transfer Learning ### Transfer Learning with SG Notebook - Semantic Segmentation Learn more about SuperGradients transfer learning or fine tuning abilities with our Citiscapes pre-trained RegSeg48 fine tuning into a sub-dataset of Supervisely example notebook on Google Colab for an easy to use tutorial using free GPU hardware
Segmentation Transfer Learning in Google Colab Download notebook View source on GitHub


## Knowledge Distillation Training ### Knowledge Distillation Training Quick Start with SG Notebook - ResNet18 example Knowledge Distillation is a training technique that uses a large model, teacher model, to improve the performance of a smaller model, the student model. Learn more about SuperGradients knowledge distillation training with our pre-trained BEiT base teacher model and Resnet18 student model on CIFAR10 example notebook on Google Colab for an easy to use tutorial using free GPU hardware
KD Training 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](https://developer.nvidia.com/cuda-11.2.0-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu) - CuDNN >= 8.1.x - Nvidia Driver with CUDA >= 11.2 support (≥460.x)
### Quick Installation
Install stable version using PyPi See in [PyPi](https://pypi.org/project/super-gradients/) ```bash pip install super-gradients ``` That's it !
Install using GitHub ```bash pip install git+https://github.com/Deci-AI/super-gradients.git@stable ```
## Computer Vision Models - Pretrained Checkpoints ### Pretrained Classification PyTorch Checkpoints | Model | Dataset | Resolution | Top-1 | Top-5 | Latency (HW)*T4 | Latency (Production)**T4 |Latency (HW)*Jetson Xavier NX | Latency (Production)**Jetson Xavier NX | Latency Cascade Lake | |------------ | ------ | ---------- |----------- | ----------- | ----------- |---------- |----------- | ----------- | :------: | | ViT base | ImageNet21K | 224x224 | 84.15 | - |**4.46ms** |**4.60ms** | **-** * |**-**|**57.22ms** | | ViT large | ImageNet21K | 224x224 | 85.64 | - |**12.81ms** |**13.19ms** | **-** * |**-**|**187.22ms** | | BEiT | ImageNet21K | 224x224 | - | - |**-ms** |**-ms** | **-** * |**-**|**-ms** | | EfficientNet B0 | ImageNet | 224x224 | 77.62 | 93.49 |**0.93ms** |**1.38ms** | **-** * |**-**|**3.44ms** | | RegNet Y200 | ImageNet |224x224 | 70.88 | 89.35 |**0.63ms** | **1.08ms** | **2.16ms** |**2.47ms**|**2.06ms** | | RegNet Y400 | ImageNet |224x224 | 74.74 | 91.46 |**0.80ms** | **1.25ms** |**2.62ms** |**2.91ms** |**2.87ms** | | RegNet Y600 | ImageNet |224x224 | 76.18 | 92.34 |**0.77ms** | **1.22ms** |**2.64ms** |**2.93ms** |**2.39ms** | | RegNet Y800 | ImageNet |224x224 | 77.07 | 93.26 |**0.74ms** | **1.19ms** |**2.77ms** |**3.04ms** |**2.81ms** | | ResNet 18 | ImageNet |224x224 | 70.6 | 89.64 |**0.52ms** | **0.95ms** |**2.01ms**|**2.30ms** |**4.56ms** | | ResNet 34 | ImageNet |224x224 | 74.13 | 91.7 |**0.92ms** |**1.34ms** |**3.57ms**|**3.87ms** | **7.64ms** | | ResNet 50 | ImageNet |224x224 | 81.91 | 93.0 |**1.03ms** | **1.44ms** | **4.78ms**|**5.10ms** |**9.25ms** | | MobileNet V3_large-150 epochs | ImageNet |224x224 | 73.79 | 91.54 |**0.67ms** | **1.11ms** |**2.42ms** |**2.71ms** |**1.76ms** | | MobileNet V3_large-300 epochs | ImageNet |224x224 | 74.52 | 91.92 |**0.67ms** | **1.11ms** |**2.42ms** |**2.71ms** |**1.76ms** | | MobileNet V3_small | ImageNet |224x224 |67.45 | 87.47 |**0.55ms** | **0.96ms** |**2.01ms** *|**2.35ms** |**1.06ms** | | MobileNet V2_w1 | ImageNet |224x224 | 73.08 | 91.1 |**0.46 ms**| **0.89ms** |**1.65ms** *|**1.90ms** | **1.56ms** | > **NOTE:**
> - Latency (HW)* - Hardware performance (not including IO)
> - Latency (Production)** - Production Performance (including IO) > - Performance measured for T4 and Jetson Xavier NX with TensorRT, using FP16 precision and batch size 1 > - Performance measured for Cascade Lake CPU with OpenVINO, using FP16 precision and batch size 1 ### Pretrained Object Detection PyTorch Checkpoints | Model | Dataset | Resolution | mAPval
0.5:0.95 | Latency (HW)*T4 | Latency (Production)**T4 |Latency (HW)*Jetson Xavier NX | Latency (Production)**Jetson Xavier NX | Latency Cascade Lake | |------------- |------ | ---------- |------ | -------- |------ | ---------- |------ | :------: | | SSD lite MobileNet v2 | COCO |320x320 |21.5 |**0.77ms** |**1.40ms**|**5.28ms** |**6.44ms** |**4.13ms**| | SSD lite MobileNet v1 | COCO |320x320 |24.3 |**1.55ms** |**2.84ms**|**8.07ms** |**9.14ms** |**22.76ms**| | YOLOX nano | COCO |640x640 |26.77|**2.47ms** |**4.09ms**|**11.49ms** |**12.97ms** |**-**| | YOLOX tiny | COCO |640x640 |37.18|**3.16ms** |**4.61ms**|**15.23ms** |**19.24ms** |**-**| | YOLOX small | COCO |640x640 |40.47 |**3.58ms** |**4.94ms**|**18.88ms** |**22.48ms** |**-**| | YOLOX medium| COCO |640x640 |46.4 |**6.40ms** |**7.65ms**|**39.22ms** |**44.5ms** |**-**| | YOLOX large | COCO |640x640 |49.25 |**10.07ms** |**11.12ms**|**68.73ms** |**77.01ms** |**-**| > **NOTE:**
> - Latency (HW)* - Hardware performance (not including IO)
> - Latency (Production)** - Production Performance (including IO) > - Latency performance measured for T4 and Jetson Xavier NX with TensorRT, using FP16 precision and batch size 1 > - Latency performance measured for Cascade Lake CPU with OpenVINO, using FP16 precision and batch size 1 ### Pretrained Semantic Segmentation PyTorch Checkpoints | Model | Dataset | Resolution | mIoU | Latency b1T4 | Latency b1T4 including IO | |--------------------- |------ | ---------- | ------ | -------- | :------: | | DDRNet 23 | Cityscapes |1024x2048 |80.26 |**7.62ms** |**25.94ms**| | DDRNet 23 slim | Cityscapes |1024x2048 |78.01 |**3.56ms** |**22.80ms**| | STDC 1-Seg50 | Cityscapes | 512x1024 |75.07 |**2.83ms** |**12.57ms**| | STDC 1-Seg75 | Cityscapes | 768x1536 |77.8 |**5.71ms** |**26.70ms**| | STDC 2-Seg50 | Cityscapes | 512x1024 |75.79 |**3.74ms** |**13.89ms** | STDC 2-Seg75 | Cityscapes | 768x1536 |78.93 |**7.35ms** |**28.18ms**| | RegSeg (exp48) | Cityscapes | 1024x2048 |78.15 |**13.09ms** |**41.88ms**| | Larger RegSeg (exp53) | Cityscapes | 1024x2048 |79.2|**24.82ms** |**51.87ms**| | 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 not including IO ## Implemented Model Architectures ### Image Classification - [DensNet (Densely Connected Convolutional Networks)](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/densenet.py) - Densely Connected Convolutional Networks [https://arxiv.org/pdf/1608.06993.pdf](https://arxiv.org/pdf/1608.06993.pdf) - [DPN](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/dpn.py) - Dual Path Networks [https://arxiv.org/pdf/1707.01629](https://arxiv.org/pdf/1707.01629) - [EfficientNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/efficientnet.py) - [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946) - [GoogleNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/googlenet.py) - [https://arxiv.org/pdf/1409.4842](https://arxiv.org/pdf/1409.4842) - [LeNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/lenet.py) - [https://yann.lecun.com/exdb/lenet/](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) - [MobileNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenet.py) - Efficient Convolutional Neural Networks for Mobile Vision Applications [https://arxiv.org/pdf/1704.04861](https://arxiv.org/pdf/1704.04861) - [MobileNet v2](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenetv2.py) - [https://arxiv.org/pdf/1801.04381](https://arxiv.org/pdf/1801.04381) - [MobileNet v3](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenetv3.py) - [https://arxiv.org/pdf/1905.02244](https://arxiv.org/pdf/1905.02244) - [PNASNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/pnasnet.py) - Progressive Neural Architecture Search Networks [https://arxiv.org/pdf/1712.00559](https://arxiv.org/pdf/1712.00559) - [Pre-activation ResNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/preact_resnet.py) - [https://arxiv.org/pdf/1603.05027](https://arxiv.org/pdf/1603.05027) - [RegNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/regnet.py) - [https://arxiv.org/pdf/2003.13678.pdf](https://arxiv.org/pdf/2003.13678.pdf) - [RepVGG](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/repvgg.py) - Making VGG-style ConvNets Great Again [https://arxiv.org/pdf/2101.03697.pdf](https://arxiv.org/pdf/2101.03697.pdf) - [ResNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/resnet.py) - Deep Residual Learning for Image Recognition [https://arxiv.org/pdf/1512.03385](https://arxiv.org/pdf/1512.03385) - [ResNeXt](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/resnext.py) - Aggregated Residual Transformations for Deep Neural Networks [https://arxiv.org/pdf/1611.05431](https://arxiv.org/pdf/1611.05431) - [SENet ](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/senet.py) - Squeeze-and-Excitation Networks[https://arxiv.org/pdf/1709.01507](https://arxiv.org/pdf/1709.01507) - [ShuffleNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/shufflenet.py) - [https://arxiv.org/pdf/1707.01083](https://arxiv.org/pdf/1707.01083) - [ShuffleNet v2](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/shufflenetv2.py) - Efficient Convolutional Neural Network for Mobile Devices[https://arxiv.org/pdf/1807.11164](https://arxiv.org/pdf/1807.11164) - [VGG](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/vgg.py) - Very Deep Convolutional Networks for Large-scale Image Recognition [https://arxiv.org/pdf/1409.1556](https://arxiv.org/pdf/1409.1556) ### Object Detection - [CSP DarkNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/csp_darknet53.py) - [DarkNet-53](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/darknet53.py) - [SSD (Single Shot Detector)](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/ssd.py) - [https://arxiv.org/pdf/1512.02325](https://arxiv.org/pdf/1512.02325) - [YOLOX](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/yolox.py) - [https://arxiv.org/abs/2107.08430](https://arxiv.org/abs/2107.08430) ### Semantic Segmentation - [DDRNet (Deep Dual-resolution Networks)](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/ddrnet.py) - [https://arxiv.org/pdf/2101.06085.pdf](https://arxiv.org/pdf/2101.06085.pdf) - [LadderNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/laddernet.py) - Multi-path networks based on U-Net for medical image segmentation [https://arxiv.org/pdf/1810.07810](https://arxiv.org/pdf/1810.07810) - [RegSeg](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/regseg.py) - Rethink Dilated Convolution for Real-time Semantic Segmentation [https://arxiv.org/pdf/2111.09957](https://arxiv.org/pdf/2111.09957) - [ShelfNet](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/shelfnet.py) - [https://arxiv.org/pdf/1811.11254](https://arxiv.org/pdf/1811.11254) - [STDC](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/stdc.py) - Rethinking BiSeNet For Real-time Semantic Segmentation [https://arxiv.org/pdf/2104.13188](https://arxiv.org/pdf/2104.13188) ## Documentation Check SuperGradients [Docs](https://deci-ai.github.io/super-gradients/welcome.html) for full documentation, user guide, and examples. ## Contributing To learn about making a contribution to SuperGradients, please see our [Contribution page](CONTRIBUTING.md). Our awesome contributors:

Made with [contrib.rocks](https://contrib.rocks). ## Citation If you are using SuperGradients library or benchmarks 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]( https://join.slack.com/t/supergradients-comm52/shared_invite/zt-10vz6o1ia-b_0W5jEPEnuHXm087K~t8Q) * To report a bug, [file an issue](https://github.com/Deci-AI/super-gradients/issues) on GitHub. * Join the [SG Newsletter](https://www.supergradients.com/#Newsletter) for staying up to date with new features and models, important announcements, and upcoming events. * For a short meeting with us, use this [link](https://calendly.com/ofer-baratz-deci/15min) and choose your preferred time. ## License This project is released under the [Apache 2.0 license](LICENSE). __________________________________________________________________________________________________________ ## Deci Platform Deci Platform is our end to end platform for building, optimizing and deploying deep learning models to production. Sign up for our [FREE Community Tier](https://console.deci.ai/) to enjoy immediate improvement in throughput, latency, memory footprint and model size. Features: - Automatically compile and quantize your models with just a few clicks (TensorRT, OpenVINO). - Gain up to 10X improvement in throughput, latency, memory and model size. - Easily benchmark your models’ performance on different hardware and batch sizes. - Invite co-workers to collaborate on models and communicate your progress. - Deci supports all common frameworks and Hardware, from Intel CPUs to Nvidia's GPUs and Jetsons. Sign up for Deci Platform for free [here](https://console.deci.ai/)