13.7.4. Training the model

With a labelled dataset in hand, training is a guided flow on the Train page: lock in a dataset version, pick an architecture, and hand the run off to Roboflow’s servers.

13.7.4.1. The dataset version

Before training, Roboflow builds a dataset version – a frozen snapshot of the images plus two transforms applied on the way in:

  • Preprocessing resizes every image to the resolution the model trains at. Keep that resolution small: the camera runs small models, and a detector trained at a modest resolution fits the camera’s memory and runs fast.

  • Augmentation synthesizes extra training images by perturbing the originals – flips, brightness and exposure shifts, blur, noise. Each augmentation teaches the model to tolerate a real variation it will meet on the camera, which stretches a small hand-captured dataset much further.

Roboflow's saturation augmentation settings, previewing the original image alongside reduced and increased saturation versions

An augmentation preview: each option shows what it does to a sample image before you commit it to the version.

Match the augmentations to variations the camera will actually see. Brightness and exposure earn their place – lighting changes constantly. Skip ones that never happen in your setup; a camera bolted in place never sees a vertical flip, so flip augmentation only dilutes the dataset.

13.7.4.2. Choosing an architecture

Next, pick the model architecture. Roboflow offers several, each with a size selector trading accuracy against speed.

Roboflow's Select Architecture page with Roboflow RF-DETR, YOLO26, Roboflow 3.0, and YOLOv11 options, each with a model-size dropdown

The architecture choices – each with a size selector trading accuracy against inference speed.

For the camera, pick Roboflow 3.0. It is YOLOv8 under the hood, and the camera ships a YOLOv8 post-processor in ml.postprocessing.ultralytics, so its output decodes with no extra code on your side. Choose the Fast size – it fits the camera’s memory and runs at a usable frame rate.

13.7.4.3. Running the training

Start the run and training happens on Roboflow’s servers – usually well under an hour for a small dataset, with an email when it is done. The version page then shows the training graphs and the accuracy metrics: mAP, precision, and recall.

Roboflow's trained-model version page showing the metrics panel with mAP, precision, recall, and F1, above the Deploy Your Model section

The trained model with its accuracy metrics. From here, the Visualize page also runs it on test images or a webcam for a quick sanity check.

If the numbers are good, the model is ready to deploy. If not, the fix is usually more or more varied data – capture another clip, label it, and train a new version.