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v1.1 — Retrained model + all weights

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@crispianm crispianm released this 21 Jul 11:41
v1.1
9bbd8bb

Every weight needed to run DaBiT (training, evaluation, or inference), plus a retrained model that improves on the paper — no architectural change, purely more and more diverse clean training video.

Models

Model PSNR ↑ SSIM ↑ LPIPS ↓ tOF ↓
dabit.pth — original paper model 28.48 0.841 0.242 1.189
dabit_retrained.pth — retrained, expanded data 29.17 0.858 0.217 1.103

Mean over all 90 DAVIS-Blur sequences, full fp32 inference.

dabit_retrained.pth uses the paper architecture and recipe, retrained for 300k iterations on a ~1.4M-frame clean-video corpus (YouTube-VOS + BVI-DVC + TartanAir-V2 + Virtual KITTI 2). +0.69 dB PSNR over the released paper weights. For the perceptually-tuned variant, see v1.2.

Dependency weights (required to run either model)

  • raft.pth — RAFT optical flow (redistributed unchanged from princeton-vl/RAFT, BSD-3)
  • depth_anything_v2_vits.pth — used at train/test time
  • depth_anything_v2_vitb.pth, depth_anything_v2_vitl.pth — for get_depths.py (Depth Anything V2, Apache-2.0)

Install

mkdir -p weights && gh release download v1.1 -R crispianm/DaBiT -D weights
python test_dabit.py --model weights/dabit_retrained.pth
Assets 8
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