You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
87 lines
5.8 KiB
87 lines
5.8 KiB
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
|
|
|
|
**Val mode** is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a
|
|
validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters
|
|
of the model to improve its performance.
|
|
|
|
!!! tip "Tip"
|
|
|
|
* 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()`
|
|
|
|
## Usage Examples
|
|
|
|
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
|
|
training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
|
|
|
|
!!! example ""
|
|
|
|
=== "Python"
|
|
|
|
```python
|
|
from ultralytics import YOLO
|
|
|
|
# Load a model
|
|
model = YOLO('yolov8n.pt') # load an official model
|
|
model = YOLO('path/to/best.pt') # load a custom model
|
|
|
|
# Validate the model
|
|
metrics = model.val() # no arguments needed, dataset and settings remembered
|
|
metrics.box.map # map50-95
|
|
metrics.box.map50 # map50
|
|
metrics.box.map75 # map75
|
|
metrics.box.maps # a list contains map50-95 of each category
|
|
```
|
|
=== "CLI"
|
|
|
|
```bash
|
|
yolo detect val model=yolov8n.pt # val official model
|
|
yolo detect val model=path/to/best.pt # val custom model
|
|
```
|
|
|
|
## Arguments
|
|
|
|
Validation settings for YOLO models refer to the various hyperparameters and configurations used to
|
|
evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and
|
|
accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed
|
|
during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation
|
|
process include the size and composition of the validation dataset and the specific task the model is being used for. It
|
|
is important to carefully tune and experiment with these settings to ensure that the model is performing well on the
|
|
validation dataset and to detect and prevent overfitting.
|
|
|
|
| Key | Value | Description |
|
|
|---------------|---------|--------------------------------------------------------------------|
|
|
| `data` | `None` | path to data file, i.e. coco128.yaml |
|
|
| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) |
|
|
| `batch` | `16` | number of images per batch (-1 for AutoBatch) |
|
|
| `save_json` | `False` | save results to JSON file |
|
|
| `save_hybrid` | `False` | save hybrid version of labels (labels + additional predictions) |
|
|
| `conf` | `0.001` | object confidence threshold for detection |
|
|
| `iou` | `0.6` | intersection over union (IoU) threshold for NMS |
|
|
| `max_det` | `300` | maximum number of detections per image |
|
|
| `half` | `True` | use half precision (FP16) |
|
|
| `device` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
|
|
| `dnn` | `False` | use OpenCV DNN for ONNX inference |
|
|
| `plots` | `False` | show plots during training |
|
|
| `rect` | `False` | rectangular val with each batch collated for minimum padding |
|
|
| `split` | `val` | dataset split to use for validation, i.e. 'val', 'test' or 'train' |
|
|
|
|
## Export Formats
|
|
|
|
Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument,
|
|
i.e. `format='onnx'` or `format='engine'`.
|
|
|
|
| Format | `format` Argument | Model | Metadata |
|
|
|--------------------------------------------------------------------|-------------------|---------------------------|----------|
|
|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ |
|
|
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ |
|
|
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ |
|
|
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ |
|
|
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ |
|
|
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ |
|
|
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ |
|
|
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ |
|
|
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ |
|
|
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ |
|
|
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ |
|
|
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ |
|