dabit_perceptual.pth — the v1.1 dabit_retrained.pth model fine-tuned for 50k iterations with an added LPIPS-VGG perceptual loss and a flow-warped, occlusion-masked temporal-consistency loss (Lai et al., ECCV 2018). Config: configs/dabit_ft.json, losses.perc_weight / losses.temp_weight.
Result (all 90 DAVIS-Blur sequences, fp32)
| Model | PSNR ↑ | SSIM ↑ | LPIPS ↓ | tOF ↓ |
|---|---|---|---|---|
| v1.1 retrain | 29.17 | 0.858 | 0.217 | 1.103 |
| v1.2 perceptual | 28.99 | 0.856 | 0.173 | 0.882 |
Trades ~0.18 dB PSNR for 20% lower LPIPS (sharper perceptual detail) and 20% lower tOF (smoother motion). Choose this model for perceptual quality, the v1.1 retrain for peak PSNR/SSIM.
Install
Requires the RAFT + Depth Anything V2 dependency weights from v1.1:
mkdir -p weights
gh release download v1.2 -R crispianm/DaBiT -D weights # perceptual model
gh release download v1.1 -R crispianm/DaBiT -D weights # deps (raft, depth-anything)
python test_dabit.py --model weights/dabit_perceptual.pth