validation time is too long

[Yung-Chih-Lo]

Based on the picture shown below, the validation time seems excessively long. Is this normal for training?

CPU：AMD R9 3900 GPU：4070ti 12GB RAM：32GB

task: detect mode: train model: yolov10b.pt data: ../coco128.yaml epochs: 500 time: null patience: 100 batch: 12 imgsz: 640 save: true save_period: -1 val_period: 1 cache: false device: null workers: 8 project: null name: train exist_ok: false pretrained: true optimizer: auto verbose: true seed: 0 deterministic: true single_cls: false rect: false cos_lr: false close_mosaic: 10 resume: false amp: true fraction: 1.0 profile: false freeze: null multi_scale: false overlap_mask: true mask_ratio: 4 dropout: 0.0 val: true split: val save_json: false save_hybrid: false conf: null iou: 0.7 max_det: 300 half: false dnn: false plots: true source: null vid_stride: 1 stream_buffer: false visualize: false augment: false agnostic_nms: false classes: null retina_masks: false embed: null show: false save_frames: false save_txt: false save_conf: false save_crop: false show_labels: true show_conf: true show_boxes: true line_width: null format: torchscript keras: false optimize: false int8: false dynamic: false simplify: false opset: null workspace: 4 nms: false lr0: 0.01 lrf: 0.01 momentum: 0.937 weight_decay: 0.0005 warmup_epochs: 3.0 warmup_momentum: 0.8 warmup_bias_lr: 0.1 box: 7.5 cls: 0.5 dfl: 1.5 pose: 12.0 kobj: 1.0 label_smoothing: 0.0 nbs: 64 hsv_h: 0.015 hsv_s: 0.7 hsv_v: 0.4 degrees: 0.0 translate: 0.1 scale: 0.5 shear: 0.0 perspective: 0.0 flipud: 0.0 fliplr: 0.5 bgr: 0.0 mosaic: 1.0 mixup: 0.0 copy_paste: 0.0 auto_augment: randaugment erasing: 0.4 crop_fraction: 1.0 cfg: null tracker: botsort.yaml

[leonnil]

Thank you for your interest! It's not normal during training if you have much less images in validation set.

[Yung-Chih-Lo]

Thank you for your attention. So how can I fix this problem?

[leonnil]

Are you training on custom data? Please check that your validation path is correct and make sure you are validating on GPU device. Using cache=True and increasing your batch size might help.