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92 lines
6.9 KiB
92 lines
6.9 KiB
--- |
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comments: true |
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description: Validate and improve YOLOv8n model accuracy on COCO128 and other datasets using hyperparameter & configuration tuning, in Val mode. |
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keywords: Ultralytics, YOLO, YOLOv8, Val, Validation, Hyperparameters, Performance, Accuracy, Generalization, COCO, Export Formats, PyTorch |
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--- |
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"> |
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**Val mode** is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a |
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validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters |
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of the model to improve its performance. |
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!!! tip "Tip" |
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* YOLOv8 models automatically remember their training settings, so you can validate a model at the same image size and on the original dataset easily with just `yolo val model=yolov8n.pt` or `model('yolov8n.pt').val()` |
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## Usage Examples |
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Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's |
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training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments. |
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!!! example "" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO('yolov8n.pt') # load an official model |
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model = YOLO('path/to/best.pt') # load a custom model |
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# Validate the model |
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metrics = model.val() # no arguments needed, dataset and settings remembered |
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metrics.box.map # map50-95 |
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metrics.box.map50 # map50 |
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metrics.box.map75 # map75 |
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metrics.box.maps # a list contains map50-95 of each category |
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``` |
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=== "CLI" |
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```bash |
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yolo detect val model=yolov8n.pt # val official model |
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yolo detect val model=path/to/best.pt # val custom model |
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``` |
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## Arguments |
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Validation settings for YOLO models refer to the various hyperparameters and configurations used to |
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evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and |
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accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed |
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during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation |
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process include the size and composition of the validation dataset and the specific task the model is being used for. It |
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is important to carefully tune and experiment with these settings to ensure that the model is performing well on the |
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validation dataset and to detect and prevent overfitting. |
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| Key | Value | Description | |
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|---------------|---------|--------------------------------------------------------------------| |
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| `data` | `None` | path to data file, i.e. coco128.yaml | |
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| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
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| `batch` | `16` | number of images per batch (-1 for AutoBatch) | |
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| `save_json` | `False` | save results to JSON file | |
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| `save_hybrid` | `False` | save hybrid version of labels (labels + additional predictions) | |
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| `conf` | `0.001` | object confidence threshold for detection | |
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| `iou` | `0.6` | intersection over union (IoU) threshold for NMS | |
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| `max_det` | `300` | maximum number of detections per image | |
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| `half` | `True` | use half precision (FP16) | |
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| `device` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
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| `dnn` | `False` | use OpenCV DNN for ONNX inference | |
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| `plots` | `False` | show plots during training | |
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| `rect` | `False` | rectangular val with each batch collated for minimum padding | |
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| `split` | `val` | dataset split to use for validation, i.e. 'val', 'test' or 'train' | |
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## Export Formats |
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Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, |
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i.e. `format='onnx'` or `format='engine'`. |
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| Format | `format` Argument | Model | Metadata | Arguments | |
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|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------| |
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` | |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` | |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` | |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` | |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ | `imgsz`, `half`, `int8`, `nms` | |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` | |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` | |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` | |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` | |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` | |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` | |