Training Your First Model: Roboflow Train

How to Train Your First Object Detection Model with Roboflow Train

Written by Mohamed Traore

Last published at: October 8th, 2022

What is Roboflow Train?

After labeling your images with bounding boxes, polygons, bounding boxes and polygons, and/or Smart Polygons, and generating a dataset version, you're ready to train a model!

Roboflow offers an AutoML product called Roboflow Train. It is the easiest way to train and deploy a state of the art object detection model on your custom dataset. It's literally one click -- we'll do the rest. When your model is done training, you'll receive access to the secure Hosted Inference API to run inference on, or "interrogate", your model for predictions via your programming language of choice (or a simple demo web app), a Tensorflow JS model you can embed in your web application, and an on-device inference server you can run on edge devices like the NVIDIA Jetson, Raspberry Pi, Luxonis OAK, or an Apple iOS device.

Roboflow Train is available for Object Detection, Single-Label Classification, Multi-Label Classification, Instance Segmentation and Semantic Segmentation. The video in this post offers a tutorial for Object Detection model training.


3 Roboflow Train credits are included in your workspace when you create it. Training a model costs one credit and takes between 10 minutes and 24 hours depending on the size of your dataset.

More credits or dedicated plans for BusinessesContact our sales team to upgrade your plan if you need more train credits.

Credits for Students, Researchers and Hobbyists:

  • Contribute to the Roboflow Blog: Author a post, and share your work with Roboflow blog and newsletter readers, worldwide.
  • Apply for Research Credits: Additional training credits and increased account limits are available for research and education.

Getting Started with Roboflow Train

Next Steps

Roboflow Train: Understanding Training Graphs

Deployment: Using the Roboflow Inference API‍ 

Python Package for OAK Deployment‍ 

Implementing Active Learning