Previous Checkpoints

Training from Previous Checkpoint

"Previous Checkpoints" are model checkpoints from any project in a workspace that you are currently part of, and hold an Admin or Creator role in.
NOTE: When training from checkpoints, if the model checkpoint you are training from is the Fast model, then the checkpoint will be available under the Fast model option. If the model checkpoint you are training from is the Accurate model, then the checkpoint you will have available is under the Accurate option. Additionally, checkpoints are only available for Transfer Learning from the same project type.
  • You can find the Model Type for a trained version on that project version's page
This project version was trained with Roboflow Train's Fast model
To begin, navigate to the "Versions" page of your target dataset/project. Select the version you wish to train.
  • You can only start a training job from a dataset version that has not already been trained with Roboflow Train (no green checkmark is present on the version).
Next, click "Start Training" on an untrained dataset version.
Choose the Fast or Accurate model, and click "Continue."
  • If you are training a Single-Label Classification, Multi-Label Classification, Instance or Semantic Segmentation project, you will not have this Fast or Accurate option. For these project types, you can just click "Continue," and then "Start Training" to kick off your training job.
Proceed to select the model you wish to use for Transfer Learning in the Select Model field. You can choose a specific version to train from if you have trained multiple versions of the project.
Click "Start Training" to begin your training job. You will receive an email to the account email on file when the training job is complete.
You can monitor your training job's progress. The UI will show your machine starting up to begin training.

Deploying Your Model

After training, your model is ready to be used for inference and embedded in a custom application! See the Inference Documentation page for all the options.