LPIFM — Learned Perceptual Image Fusion Measure

Pairwise perceptual preference model for infrared–visible image fusion (IVIF) ranking.

Given an IR source, a VI source, and two fused candidates, LPIFM predicts A better, B better, or Tie. Pairwise decisions can be aggregated with tie-aware Bradley–Terry (T-BT) to rank a method pool.

Code and examples: github.com/HaoranLiu507/LPIFM

Files

File	Role
`lpifm_vifb_baseline_v1.pt`	Main VIFB-trained public checkpoint
`inference_config.yaml`	Decode defaults (`t`, `T_cal`, image size)

EVAFusion fine-tuned weights are not hosted here; see the Zenodo companion archive when published.

Note on checkpoint size. lpifm_vifb_baseline_v1.pt is about 2 GB because it is a full training checkpoint: three weight copies (swa / ema / model, about 0.4 GB each) plus the optimizer (about 0.8 GB). To use LPIFM only, keep swa_state_dict (+ config); about 0.4 GB is enough. Inference already loads SWA by default.

Architecture

Backbone: ConvNeXt-V2 (convnextv2_base.fcmae_ft_in22k_in1k_384 via timm)
Input size: 384 × 384
Task: source-conditioned pairwise preference scoring (ternary A / B / Tie)

Decode / inference defaults

Parameter	Value
Tie threshold `t`	`0.3`
Calibration temperature `T_cal`	`1.0`
Image size	`384`

d_cal = d / T_cal
d_cal >  0.3  → A better (0)
d_cal < -0.3  → B better (1)
otherwise     → Tie (2)

Quick start

# from the GitHub repository
python scripts/download_assets.py --source hf
python predict.py \
  --config configs/release_inference.yaml \
  --dataset_root Dataset/VIFB \
  --ckpt checkpoints/lpifm_vifb_baseline_v1.pt \
  --image_name carLight.jpg \
  --a_dir U2Fusion \
  --b_dir SeAFusion

Or download this file directly:

huggingface-cli download FengShaner/LPIFM lpifm_vifb_baseline_v1.pt \
  --local-dir checkpoints

Intended use

Pairwise perceptual preference for IVIF method ranking
Research / offline evaluation under the LPIFM protocol
Not a general-purpose IQA model for arbitrary natural-image aesthetics

Limitations

Trained and validated under a specific preference-collection and ranking protocol
Protocol mismatch (new dataset, different rater instructions, different method pools) may reduce agreement; fine-tuning may be required
Non-commercial weights license (see below)

Licenses

Artifact	License
Model weights on this Hub repo	CC BY-NC-SA 4.0
Source code on GitHub	AGPL-3.0

Weights do not inherit AGPL; code does not inherit CC BY-NC-SA.

Citation

See CITATION.cff in the GitHub repository.

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