Image Resize in Validation about ultralytics

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

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[Rbrq03]

Additional log are shown below:

engine/trainer: task=detect, mode=train, model=yolov10n.yaml, data=coco.yaml, epochs=100, time=None, patience=100,
 batch=256, imgsz=512, save=True, save_period=-1, val_period=1, cache=False, device=[0, 1, 2, 3, 4, 5, 6, 7], workers=8, 
project=None, name=train35, exist_ok=False, pretrained=True, optimizer=SGD, verbose=True, seed=0, deterministic=False, 
single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, 
freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False,
 save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, 
stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, 
embed=None, 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=None, format=torchscript, keras=False, optimize=False, int8=False, 
dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.0001, 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=None, tracker=botsort.yaml,

Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /opt/data/private/hjn/fna-detection/datasets/coco/labels/train2017.cache... 117266 images, 1021 backgrounds, 0 corrupt: 100%|██████████| 118287/118287 [00:00<?, ?it/s]
val: Scanning /opt/data/private/hjn/fna-detection/datasets/coco/labels/val2017.cache... 4952 images, 48 backgrounds, 0 corrupt: 100%|██████████| 5000/5000 [00:00<?, ?it/s]
Plotting labels to /opt/data/private/hjn/fna-detection/yolov10/runs/detect/train35/labels.jpg... 
optimizer: SGD(lr=0.01, momentum=0.937) with parameter groups 95 weight(decay=0.0), 108 weight(decay=0.002), 107 bias(decay=0.0)
Image sizes 512 train, 512 val
Using 64 dataloader workers
Logging results to /opt/data/private/hjn/fna-detection/yolov10/runs/detect/train35
Starting training for 100 epochs...

      Epoch    GPU_mem     box_om     cls_om     dfl_om     box_oo     cls_oo     dfl_oo  Instances       Size
      1/100      5.42G      3.562      5.044      3.487      3.284      6.018      3.206         30        512: 100%|██████████| 463/463 [01:27<00:00,  5.29it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0%|          | 0/79 [00:00<?, ?it/s]torch.Size([64, 3, 288, 544])

[glenn-jocher]

@Rbrq03 hello!

Thank you for reaching out with your question regarding the imgsz argument in validation.

The imgsz argument is used to specify the target image size for both training and validation. When you set imgsz=512, it indicates that the images should be resized to 512x512 pixels. However, in practice, the actual image dimensions may vary slightly due to the aspect ratio preservation and padding applied during preprocessing.

From your provided logs, it seems that the images are being resized to dimensions close to 512x512 but not exactly square, resulting in shapes like torch.Size([64, 3, 288, 544]). This discrepancy is due to the rect argument, which is set to False by default. When rect is False, the images are resized while preserving their aspect ratios, and padding is added to match the target size.

To ensure that the images are resized to exactly 512x512, you can set the rect argument to True. This will enforce rectangular training and validation, resizing the images to the specified dimensions without preserving the aspect ratio.

Here’s how you can modify your training script to achieve this:

from ultralytics import YOLOv10
import torch

model = YOLOv10("yolov10n.yaml")
model.train(
    data="coco.yaml",
    epochs=100,
    batch=256,
    imgsz=512,
    optimizer="SGD",
    lr0=0.01,
    lrf=0.0001,
    plots=True,
    fna=True,
    device=[0, 1, 2, 3, 4, 5, 6, 7],
    rect=True  # Add this line to enforce rectangular resizing
)

By setting rect=True, the images will be resized to exactly 512x512 during validation, ensuring consistent dimensions.

If you have any further questions or need additional assistance, feel free to ask. Happy training! 😊

[Rbrq03]

Thanks @glenn-jocher! What I further concern is, the shape I print is excepted to be 512x512 as it will be feed into the model forward directly. So it should be the shape which is after padding? What cause this problem and how should i do?

[glenn-jocher]

Hello @Rbrq03,

Thank you for your follow-up question!

To ensure that your images are resized to exactly 512x512 before being fed into the model, you should set the rect argument to True in your training script. This will enforce rectangular resizing, ensuring that the images are resized to the specified dimensions without preserving the aspect ratio.

Here’s how you can modify your training script:

from ultralytics import YOLOv10
import torch

model = YOLOv10("yolov10n.yaml")
model.train(
    data="coco.yaml",
    epochs=100,
    batch=256,
    imgsz=512,
    optimizer="SGD",
    lr0=0.01,
    lrf=0.0001,
    plots=True,
    fna=True,
    device=[0, 1, 2, 3, 4, 5, 6, 7],
    rect=True  # Add this line to enforce rectangular resizing
)

By setting rect=True, the images will be resized to exactly 512x512 during validation, ensuring consistent dimensions that match your expectation.

If you continue to experience issues, please ensure you are using the latest versions of torch and ultralytics. If the problem persists, providing a minimum reproducible code example would be very helpful for us to investigate further. You can find more details on how to create one here: https://docs.ultralytics.com/help/minimum_reproducible_example.

Feel free to reach out if you have any more questions! 😊

[Rbrq03]

Thanks @plashchynski ! it solves my problem.