| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - image-to-image |
| tags: |
| - face-restoration |
| - reference-based-restoration |
| - training-data |
| - snapcv |
| pretty_name: RefineFIR TrainingData |
| --- |
| |
| # RefineFIR TrainingData |
|
|
| This repository contains training artifacts for the RefineFIR reimplementation: |
|
|
| - `celebref_list_256_jw_0403.pkl`: same-identity `(source_id, target_id)` training pairs. |
| - `celebaref_warped_snapCV_jw_0403.zip`: precomputed Snap/XCV warped reference images for the above pairs. |
|
|
| The clean image root used by `train.py` is expected to be arranged as `CelebHQRefForRelease/<identity>/<image>.png`, where pair ids such as `00001_2` resolve to `CelebHQRefForRelease/00001/2.png`. |
|
|
| The historical training pipeline generated these warped references with Snap/XCV. The public inference code uses MediaPipe, but training can consume the precomputed warped images directly without releasing Snap/XCV code. |
|
|
