Add LPIFM VIFB baseline checkpoint and model card

README.md

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 ---
 license: cc-by-nc-sa-4.0
+library_name: pytorch
+tags:
+  - image-fusion
+  - infrared-visible
+  - perceptual-quality
+  - pairwise-preference
+  - bradley-terry
+pipeline_tag: image-classification
 ---
+
+# 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](https://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.
+
+## 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` |
+
+```text
+d_cal = d / T_cal
+d_cal >  0.3  → A better (0)
+d_cal < -0.3  → B better (1)
+otherwise     → Tie (2)
+```
+
+## Quick start
+
+```bash
+# 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:
+
+```bash
+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](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
+| Source code on GitHub | [AGPL-3.0](https://github.com/HaoranLiu507/LPIFM/blob/main/LICENSE) |
+
+Weights do **not** inherit AGPL; code does **not** inherit CC BY-NC-SA.
+
+## Links
+
+- GitHub: https://github.com/HaoranLiu507/LPIFM
+- Hugging Face: https://huggingface.co/FengShaner/LPIFM
+- Zenodo archival DOI: to be added after deposit publication
+
+## Citation
+
+See [`CITATION.cff`](https://github.com/HaoranLiu507/LPIFM/blob/main/CITATION.cff) in the GitHub repository.

inference_config.yaml

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+# LPIFM VIFB baseline — inference defaults (Hub companion)
+# Full training / architecture config lives on GitHub:
+#   https://github.com/HaoranLiu507/LPIFM/blob/main/configs/release_inference.yaml
+
+model:
+  backbone: ConvNeXt-V2  # timm: convnextv2_base.fcmae_ft_in22k_in1k_384
+
+image_size: 384
+
+inference:
+  # Ternary decode: d_cal = d / T_cal
+  #   d_cal >  t  → A better (0)
+  #   d_cal < -t  → B better (1)
+  #   otherwise   → Tie (2)
+  tie_threshold_t: 0.3
+  calibration_temperature_Tcal: 1.0

lpifm_vifb_baseline_v1.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:28c98f3fdb1da138c3bc7a0ea952b2707dd25a9ec1531fd6375064ed3349cd3a
+size 2105942627