Colourization Input — Mug Greyscale (512px)
Greyscale input for colourizing the mug scene. Pair with the colour ground truth in this group.
Specifications
- Width
- 512
- Height
- 512
- Role
- greyscale input
- Subject
- mug
- Seed
- 502
- Reference
- img-colourize-mug-colour
What is a .png file?
PNG (Portable Network Graphics) is a raster image format using lossless DEFLATE compression. It supports full 8- or 16-bit-per-channel truecolor, palette, and greyscale modes with an optional alpha channel, but no animation. It is the standard choice for screenshots, logos, and graphics with sharp edges or transparency.
How to use this file
Use an example PNG to test image decoders, alpha-compositing, thumbnail generators, and format converters, or to verify that a pipeline preserves transparency and color depth on round-trip.
How to use this file for testing
“Colourization Input — Mug Greyscale (512px)” is a deterministic Novus Examples fixture for Colourization, Image pipeline QA. True-colour ground truths paired with greyscale and sepia inputs for measuring photo colourization models and filters against known colour references.
Documented properties for this file: 512×512 · seed 502 · greyscale input. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
For image AI or enhancement tools, run the model or filter on this file and diff against the clean or ground-truth companion in the same group when available. Keep seeds and documented damage parameters in your evaluation notes so regressions are attributable.
Run your enhancement model on the degraded input and diff against the clean ground-truth companion in the same group; keep the documented degradation parameters and seed in your eval notes so scores are reproducible.
Code examples
<img src="mug-greyscale-input.png" alt="Example image" width="640" loading="lazy">Related files
- pngColourization GT — Fruit (512px)True-colour ground truth for the fruit colourization suite. Compare a colourizer's output against this reference to score accuracy.
- pngColourization GT — Plant (512px)True-colour ground truth for the plant colourization suite. Compare a colourizer's output against this reference to score accuracy.
- pngColourization Input — Fruit Greyscale (512px)Greyscale input for colourizing the fruit scene. Pair with the colour ground truth in this group.
- pngColourization Input — Fruit Sepia (512px)Sepia input for colourizing the fruit scene. Pair with the colour ground truth in this group.
- pngColourization Input — Plant Greyscale (512px)Greyscale input for colourizing the plant scene. Pair with the colour ground truth in this group.
- pngColourization Input — Plant Sepia (512px)Sepia input for colourizing the plant scene. Pair with the colour ground truth in this group.
Generated by generation/images_enhance_wave_a.py. Free for any use, no attribution required — license.