| Scenario | Original (px) | Target rule | Output (px) | Why it’s used |
|---|---|---|---|---|
| Classification baseline | 1920×1080 | Contain inside 224×224 | 224×126 | Matches compact encoders; minimizes compute. |
| Detection training | 4032×3024 | Scale 25% | 1008×756 | Reduces VRAM while keeping object detail. |
| Segmentation (stride aligned) | 1024×1024 | Snap to multiple of 32 | 1024×1024 | Avoids padding artifacts in downsampling paths. |
| Generative workflows | 3000×2000 | Cover 512×512 | 768×512 | Fits latent grids; cropping handled separately. |
- scale = percent / 100 or scale = factor.
- newWidth = originalWidth × scale, newHeight = originalHeight × scale.
- Target width mode (ratio locked): scale = targetWidth / originalWidth.
- Target height mode (ratio locked): scale = targetHeight / originalHeight.
- Fit into box (contain): scale = min(boxW/origW, boxH/origH).
- Fit into box (cover): scale = max(boxW/origW, boxH/origH).
- Tensor bytes estimate: W × H × channels × bytesPerElement × batch.
- Snap: snapped = multiple × round(value / multiple) (or up/down).
- Enter the original pixel width and height from your dataset.
- Select a resize mode that matches your pipeline constraint.
- Keep aspect ratio on to avoid stretching labels and features.
- Use “Snap to multiple” when your model uses strided layers.
- Choose channels, data type, and batch to estimate memory.
- Submit to view results, then export CSV or PDF.
Dataset Standardization Across Splits
Training sets often mix camera sources and resolutions. Standardize inputs to reduce shift between train, validation, and test. A common baseline is 224×224 for classification and 640×640 for detection. Use “contain” to preserve labels, and track megapixels to compare compute budgets. Dropping from 12.0 MP to 1.0 MP can cut per-image math by roughly 12×.
Aspect Ratio Discipline for Labels
Keeping aspect ratio prevents stretched features that can harm bounding boxes and segmentation masks. When the ratio is locked, a single scale applies to width and height. For example, scaling 1920×1080 to 224×126 preserves the 16:9 geometry. Ratio changes are flagged to avoid silent distortions, and they can require re-labeling.
Fitting Strategies: Contain, Cover, Stretch
Contain uses the smaller of boxW/origW and boxH/origH, so nothing is cropped. Cover uses the larger ratio to fill the box, then you can crop downstream. Stretch forces exact box dimensions and is best reserved for non-spatial signals. For a 512×512 box, a 3000×2000 image becomes 768×512 with cover, then a centered crop yields 512×512.
Stride Multiples for Efficient Networks
Many encoders downsample by powers of two. Snapping dimensions to 16 or 32 reduces padding and keeps feature maps aligned. If a resized width becomes 513, snapping to 32 with “nearest” produces 512, while “up” produces 544. This choice affects speed, VRAM, and output sharpness, especially in UNet blocks with skip connections.
Tensor Memory and Batch Planning
Input tensors scale with width × height × channels × bytes. Moving from float32 to float16 halves storage, and using grayscale reduces channels from 3 to 1. The calculator estimates per-batch memory so you can align image size with GPU limits, especially when batch sizes exceed 8. As a reference, 256×256×3 float16 is about 0.38 MB per image.
Practical Targets for Modern Pipelines
Diffusion and generative systems frequently use 512, 768, or 1024 square sizes, while video frames may target 720p or 1080p. Start by preventing upscaling for low-resolution data, then scale down until memory and throughput meet goals. Export CSV or PDF reports to document the final choice. Record the snap multiple you used, so inference matches training exactly. This improves reproducibility across experiments too.
FAQs
What does “snap to multiple” do?
It rounds final width and height to a chosen multiple such as 16 or 32. This helps stride-based networks reduce padding, align feature maps, and stabilize runtime shapes across batches.
When should I use “contain” versus “cover”?
Use contain when you must avoid cropping, such as segmentation masks or precise bounding boxes. Use cover when you will crop later and want the resized image to fully fill a fixed box.
Why avoid upscaling for training data?
Upscaling invents detail and can amplify noise, especially in low-light images. Keeping upscaling off preserves native texture statistics and often improves generalization for small datasets.
How accurate is the memory estimate?
It estimates raw tensor storage for one batch: W×H×channels×bytes×batch. Framework overhead, activations, gradients, and optimizer states can require substantially more memory during training.
Can I resize without preserving aspect ratio?
Yes, but it can distort shapes and labels. Only unlock aspect ratio when your model requires an exact input size and your task tolerates geometric warping, like some style or feature extraction tasks.
Which sizes are common for modern vision pipelines?
224, 256, 320, 512, 640, and 1024 are frequent targets. Choose based on task detail needs, GPU limits, and model stride. Snapping to 32 is a practical default for many backbones.