Quick-start
Train your first custom YOLOv11 model in minutes.
🚀 Quick-start Guide
New to Ocular Foundry’s model training capabilities? Follow this five-minute path to train and evaluate your first custom model. Once you are comfortable, circle back to the advanced pages for deep dives.
1. Requirements
| Item | Why you need it |
|---|---|
| At least one Version | Training uses the frames and annotations in a specific version. |
| Member role permissions | You must have member rights to launch training jobs. |
2. Open the Training tab
- Navigate to the Versions tab in a project.
- Select the version you wish to use to train the model
- Click on the Train in the right-hand corner of that version’s details page. You will see the following popup to configure and start a training run:
3. Adjust Configuration Settings
Choose the model type, model variant, number of epochs, and image size.
| Setting | Default | Rationale |
|---|---|---|
| Model Variant | m | Best blend of speed and accuracy. |
| Epochs | 10 | Enough to verify the workflow. |
| Image Size | 640 | Detects small objects yet fits in 8 GB VRAM. |
YOLOv11 supports detection, segmentation, pose, classification, and oriented bounding boxes (OBB). Ocular currently supports detection and segmentation models for object detection or segmentation project types, respectively; other types are on the roadmap.
4. Click Create Model
You will be redirected to the “Models” tab, where the run appears instantly in Model Training History with a processing 🔵 status. When the training run has completed, it will have a competed 🟢 status.
5. Observe Performance Metrics
Performance Metrics and loss curves will be available on the model details page after the training run has completed 🟢 . You will see the precision, recall, and mAP metrics over epochs, the confusion matrix, and previews of validation set data, and more.
Next up:
- Fine-tune settings → Model Training
- Interpret metrics → Model Evaluation
- Programmatic workflows → Notebooks