libreyolo val

Evaluates one model against one dataset split and prints the metrics. The metric set depends on the model's task, and the numbers are the ones a benchmark row is built from.

Command: libreyolo val
Required: model, data
Output: Metrics on stdout. Plots and COCO JSON under runs/val/exp when asked for

Synopsis

bash

libreyolo val model=<name|path> data=<dataset.yaml> [key=value ...]

Arguments are key=value pairs, and POSIX form works too, so batch=8 and --batch 8 are the same argument.

Arguments

Argument	Default	Meaning
`model`		Model weights path or CLI name. Required
`data`		Path to dataset YAML (YOLO format, e.g. `coco8.yaml`). Required
`data_dir`		Direct dataset directory, bypassing the path in the YAML
`split`	`val`	Dataset split: `val`, `test`, `train`
`batch`	`16`	Batch size
`imgsz`		Image size: `640` (square) or `480x640` (HxW). The model's own input size when unset
`conf`	`0.001`	Confidence threshold
`iou`	`0.6`	NMS IoU threshold
`max_det`	`300`	Max predictions per image after NMS
`eval_max_det`		COCO evaluator cap. The pycocotools AP@100 convention when unset
`faster_coco_eval`	`true`	Use the faster-coco-eval C++ backend for COCO metrics when installed; falls back to pycocotools
`half`	`false`	FP16 inference
`amp_dtype`	`float16`	CUDA autocast dtype when `half=true`: `float16` or `bfloat16`
`save_json`	`false`	Save COCO-format JSON results
`save_plots`	`false`	Save validation plots: metrics, per-class AP, confusion matrix, samples
`workers`	`4`	Dataloader workers
`device`	`auto`	Device
`project`	`runs/val`	Output directory root
`name`	`exp`	Experiment name
`exist_ok`	`false`	Reuse output directory
`allow_download_scripts`	`false`	Allow embedded Python in dataset YAML download blocks
`json`	`false`	JSON output to stdout
`quiet`	`false`	Suppress stderr
`verbose`	`true`	Verbose output
`help_json`	`false`	Dump command schema as JSON and exit

Examples

Basic

libreyolo val model=LibreYOLO9s.pt data=coco8.yaml

Plots and COCO JSON

libreyolo val model=LibreYOLO9s.pt data=coco8.yaml \  imgsz=640 batch=8 save_json=true save_plots=true \  project=runs/val name=yolo9s-coco8 exist_ok=true

Machine readable

libreyolo val model=LibreYOLO9s.pt data=coco8.yaml json=true quiet=true

Notes

What the metrics are

The printed set follows the model's task, and the JSON output uses the same keys.

Detection, segmentation and oriented boxes report mAP50, mAP50_95, precision and recall. Where a model predicts more than one output kind, the per-kind groups appear alongside as box_metrics, mask_metrics and obb_metrics, each carrying the same four keys.

Classification reports accuracy_top1 and accuracy_top5. Point detection reports precision, recall, f1, MLE, MAE, RMSE and mAP_sweep. Depth reports abs_rel, rmse, delta1, delta2 and delta3. Semantic segmentation reports mIoU and pixel_accuracy. Restoration reports PSNR and SSIM.

The JSON result also carries eval_backend, naming the COCO evaluation library and version that produced the numbers, so two runs can be compared knowing whether the same backend scored both.

Thresholds

The defaults here are evaluation defaults, not prediction defaults: conf is 0.001 and iou is 0.6, where libreyolo predict uses 0.25 and 0.45. Raising conf to a display threshold lowers recall and with it the mAP, so a number produced that way is not comparable to a published one.

imgsz is unset by default, which means the model's own input size. Setting it evaluates at the size given, which is how a checkpoint gets measured away from its native resolution.

Datasets that download

A dataset YAML whose download field is a URL fetches on first use with no extra permission. One that carries an embedded Python download script needs allow_download_scripts=true, and the command warns on stderr that local code execution was enabled. The bundled coco8.yaml and coco128.yaml are URL based, so they need nothing.

Output and exit codes

stdout carries the metrics; progress goes to stderr. json=true prints one object with schema_version, and quiet=true silences stderr.

The exit code is 0 on success, 2 for a usage or configuration error, 3 when the dataset cannot be found, 4 when the model cannot be loaded, and 1 for other runtime failures.

Related: libreyolo train, which runs this same evaluation on its own schedule through eval_interval.