How to train models
Learn how to use Supervisely Apps to train custom AI models, deploy them on your GPU and use in the labeling toolboxes
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Learn how to use Supervisely Apps to train custom AI models, deploy them on your GPU and use in the labeling toolboxes
Last updated
Was this helpful?
To learn more about training a model on your custom data and pre-labeling (getting predictions from) your images with the trained models, .
We will provide a step-by-step guide for training a custom model, using as an example. Supervisely offers a no-code solution for training, deploying and predicting with models directly in your web browser, leveraging user-friendly interfaces and integrated tools.
You have several options for preparing your training data:
your images, them, and then train a custom neural network model.
Import your (such as and ) and try to build neural network model directly on your custom data.
Pick we prepared for you and reproduce this tutorial from start to end.
Ensure no network configuration is needed, and the connection is secure and private.
Follow the wizard to configure the main training settings, similar to those allowed by the original repository. You can:
Choose all or a subset of classes for training.
Define training and validation splits.
Select one of the available model architectures.
Configure training hyperparameters, including augmentations.
Press the Train
button and monitor logs, charts and visualizations in real-time.
The training process generates artifacts, including model weights (checkpoints), logs, charts, additional visualizations of training batches, predictions on validation data, precision-recall curves, confusion matrices and so on. These artifacts will be automatically saved to your Team Files.
Once the model is trained, you probably want to try it on your data and evaluate its performance.
Provide the checkpoint (model weights file in .pt
format) and follow the app's instructions.
This approach gives you the ability to automatically pre-label images and then just manually correct model mistakes
The app will iterate over all images in your project, apply your model in a batch manner, and save all predicted labels to a new project.
The trained model can be easily exported and used outside the platform. Go to the directory with training artifacts in your Team Files and download the model weights in PyTorch (.pt
) format for external use.
Here is a Python example of inference:
You can check the main section of the documentation on neural networks:
Before you start training or running neural networks, you need to connect your PC or a cloud server with a GPU to Supervisely by running a simple command in your terminal. This connection allows you to train neural networks and run inference directly from the Supervisely web interface. You can find detailed instructions on how to do this .
Open the training app from your labeled data project, click the [⫶]
button → Neural Networks → YOLO → .
Use the app to deploy your model as a REST API service so it can receive images and return predictions in response.
In Supervisely you can quickly deploy custom or pretrained neural network models weights on your GPU using the in just a few clicks.
Use the app to apply your model to images or regions of interest during annotation, configure inference settings like confidence thresholds or select all or several model classes.
Use the app to pre-label all images in a project. Follow the wizard to configure settings and run batch inference (connect to the model, select model classes, configure inference settings, and preview predictions).
Now you can follow the to get predictions on images.