Train a Model
Train a model using state-of-the-art technology in the Roboflow dashboard.
Last updated
Was this helpful?
Train a model using state-of-the-art technology in the Roboflow dashboard.
Last updated
Was this helpful?
You can train computer vision models in the Roboflow interface.
Roboflow offers two training options:
Roboflow Train: Our flagship training service, ideal for creating production-ready models.
: Train models in a few minutes that are ideal for testing.
When you approve a batch of image annotations, Instant models are automatically trained. These models can be used immediately.
Models trained on Roboflow can be deployed with Inference, our on-device inference server, or in the cloud using our Serverless Hosted API with Workflows, Batch Processing with Workflows, or with your model API endpoint.
To train a computer vision model, first .
Click the "Custom Train" button to start configuring a training job:
Next, you need to select a model architecture. This is the machine learning technology used to train your model.
The model architectures you can train depend on the type of project you have set up:
Object Detection: You can train Roboflow 3.0, RF-DETR, YOLOv11, YOLOv12, and YOLO-NAS models.
Classification: ViT and ResNet.
Instance Segmentation: Roboflow 3.0 and YOLO11.
Keypoint Detection: Roboflow 3.0 and YOLO11.
Multimodal: Florence 2, PaliGemma 2, and Qwen-2.5 VL.
Choose an architecture available for your project type, then click "Continue":
Next, you need to set a size for your model.
For development and testing, we recommend Fast models. For models intended for production, we recommend Accurate. For production models that do not need to run in real time and where accuracy is essential, choose Extra Large.
Accurate and Extra Large are only available for Object Detection models.
After selecting a training option, you will be asked whether you want to train from a checkpoint. The tabs below show the configuration options for each model type.
You have three options:
Train from a Previous Checkpoint: Ideal for when you already have a working model that you want to improve.
Train from Public Checkpoint: Ideal for your first model version, or for when a previous training run did not achieve the expected results.
Train from Random Initialization: For advanced users only, this option gives you a blank slate from which to train. Most users see worse results when using this option.
Once you have chosen a Checkpoint from which to train, click Start Training.
Your dataset will then be zipped and prepared for training in the Roboflow cloud.
After your dataset has been prepared, you will receive an estimate that shows how long training will take:
The larger the dataset, and the larger the images in your dataset, the longer it will take for your model to train.
We will email you when the training process finished. In most cases, this should be under 24 hours.
You can train from checkpoints based on projects hosted on Universe (object detection only). To do so, first . Then, the project will be available as a training checkpoint in the Roboflow web application.
Training from a Checkpoint means that you are employing . Transfer Learning will initialize your model training from the model you have selected. This can help to reduce training time, and provide you with improved training scores.
Training on Roboflow is priced on the length of the train job. You can see more information on our .
If you are a student or researcher and need credits for a project on which you are working, you can .