Model Training Tab¶
Use Model Training to train YOLO pose models from the GUI.
Important scope note¶
The built-in training workflow is pose-oriented.
If you are working with a detection-only model, the usual path is to bring that checkpoint into Inference rather than train it here.
At a glance¶
| Best for | Typical output | Usually next |
|---|---|---|
Training YOLO pose checkpoints from dataset.yaml |
Weights, metrics, exportable model artifacts | Inference |
Main sections¶
1. Training paths¶
| Field | What it is |
|---|---|
| Dataset YAML Path | The dataset.yaml prepared in Setup |
| Model Save Directory | Where runs, weights, and logs are written |
2. Model and run configuration¶
| Field | What it is |
|---|---|
| Model Variant | Starting pose checkpoint |
| Run Name | Folder name for this training run |
3. Training essentials¶
Common controls include:
- epochs
- learning rate
- batch size
- image size
4. Advanced training settings¶
Use these when you need finer control over:
- optimizer choice
- weight decay
- label smoothing
- early-stop patience
- device override
5. Augmentation settings¶
Use these when you want to tune:
- HSV shifts
- rotation, translation, and scale
- flips
- mixup, mosaic, and copy-paste
6. Export and quantization¶
After training, use the export section to create deployment artifacts such as:
- TensorRT engine
- ONNX
- OpenVINO
- TorchScript
- CoreML
Model Registry integration¶
The Training tab works closely with Model Registry.
Typical behavior:
- completed runs can register
best.pt - recently trained models can be reused in
InferenceandWebcam Inference - export targets can be selected from the registry
Practical tips¶
- Start with default pose settings unless you have a reason to tune aggressively.
- Use Dataset QA before long training runs.
- Watch the Log tab during training for the first useful error message if a run fails.