| --- |
| license: apache-2.0 |
| pipeline_tag: image-to-image |
| tags: |
| - colorization |
| - unet |
| - pytorch |
| - safetensors |
| - onnx |
| datasets: |
| - johnowhitaker/imagenette2-320 |
| - detection-datasets/coco |
| --- |
| # Mini U-Net Colorizer — broader-data trained candidate |
|
|
| **Status: evaluated app-testing candidate.** Some broad color patches and |
| incorrect object hues remain. Predicted colors are not evidence of original |
| historical colors. |
|
|
| This checkpoint has 3,968,892 learned parameters and 236 fixed color bins. |
| It starts from the audited bin-mapping repair of main commit |
| `6c47ea40724d8fcd67d4f36ce837dc1cb5b1b2a8` and changes all 65 learned parameter |
| tensors. The color vocabulary is unchanged. The selected weights are update |
| 748 of a completed 1,122-update BF16 L4 run using a 17,325-photo mixed training |
| pool (11,943 Imagenette plus 5,382 COCO), batch 32, initial learning rate 1e-5, |
| weighted classification loss and frozen BatchNorm running statistics. |
|
|
| Selection compared Imagenette50 and reserved COCO200 validation images. |
| The selected model retained color strength better than spatial-loss candidates. |
| It was then scored on separate Imagenette200 and COCO-val100 checks. |
|
|
| | Test sample | Previous repaired error | This release error | Fine excess-edge reduction | |
| |---|---:|---:|---:| |
| | Imagenette 200 | 13.318 | 12.847 | 85.8% | |
| | COCO-val 100 | 15.266 | 14.532 | 84.3% | |
|
|
| Error is mean Lab chroma distance. The release includes guided8 decoding; |
| raw learned weights alone improve error by 1.97% and 3.25%, respectively. |
| Excess-edge reductions are proxies, not counts of visible blotches removed. |
| COCO is a convenience sample; older upstream training exposure is unknown. |
|
|
| ## Use the complete pipeline |
|
|
| ```bash |
| python -m pip install -r requirements.txt |
| python inference.py --model . --output-dir colorized photo.jpg |
| ``` |
|
|
| ```python |
| from PIL import Image |
| from model import load_model |
| from inference import colorize |
| model = load_model('.') |
| colorize(model, Image.open('photo.jpg')).save('colorized.png') |
| ``` |
|
|
| Defaults: temperature 0.38, guided radius 8, epsilon 0.001, one network pass. |
| The wrapper preserves aspect ratio and original luminance. Old app code that |
| only loads safetensors will not automatically gain guided filtering. |
|
|
| For ONNX without PyTorch: |
|
|
| ```bash |
| python -m pip install -r requirements-onnx.txt |
| python colorize_onnx.py --model colorizer.onnx --output-dir colorized photo.jpg |
| ``` |
|
|
| The 15.9MB ONNX graph includes the model and guided decoder, with dynamic |
| batch/spatial sizes and verified PyTorch parity. See `DEPLOYMENT.md` for the |
| Lab input contract, CPU timings, publication commands and integration limits. |
| See `RESEARCH_ROUND2.md` and `reports/round2/` for complete measured evidence. |
|
|
| ## Limitations |
|
|
| Smoothing removes fine color fluctuations but can suppress true small color |
| details, especially without luminance boundaries. Semantically wrong hues |
| remain. The coffee and rocket failure examples are retained. Browser/mobile |
| performance, video consistency and general production quality are unvalidated. |
| The separate experiment bundle contains all runs and reproduction code; GPU |
| optimizer state is not included. This is a weights-only continuation point. |
|
|
