organize fold0 checkpoint package

README.md

@@ -1,202 +1,154 @@
 ---
-base_model: llava-hf/llava-1.5-7b-hf
-library_name: peft
+pretty_name: LMOL FBP5500 Fold0 Checkpoint
+tags:
+- lmol
+- facial-beauty-prediction
+- facial-attractiveness-prediction
+- scut-fbp5500
+- llava
+- qlora
+- peft
+- checkpoint
 ---
 
-# Model Card for Model ID
+# LMOL-FBP5500 Fold0 Checkpoint
 
-<!-- Provide a quick summary of what the model is/does. -->
+This Hugging Face Dataset repository stores the fold0 checkpoint package and small result artifacts for the GitHub repository:
 
+<https://github.com/ljc-1222/Order-Learning-with-Large-Multimodal-Models-for-Facial-Beauty-Prediction>
 
+It is hosted as a Dataset repository to keep the GitHub repository lightweight. The package is not a redistribution of SCUT-FBP5500 images or labels.
 
-## Model Details
+## Paper
 
-### Model Description
+This checkpoint is for an implementation of:
 
-<!-- Provide a longer summary of what this model is. -->
+> Zhenyou Liu, Xuefeng Liang, and Jian Lin, "Large Multimodal Model is a Better Comparator on Facial Beauty Prediction," ICASSP 2025. DOI: [10.1109/ICASSP49660.2025.10889943](https://doi.org/10.1109/ICASSP49660.2025.10889943)
 
+The method uses a large multimodal model as a pairwise comparator. Given two face images, the model predicts `First.`, `Second.`, or `Similar.`, and absolute SCUT-FBP5500 beauty scores are estimated from many pairwise comparisons.
 
+## Repository Layout
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+```text
+.
+├── README.md
+├── checkpoints/
+│   └── fold0/
+│       ├── adapter_config.json
+│       ├── adapter_model.safetensors
+│       ├── projector.pt
+│       ├── tokenizer.json
+│       ├── tokenizer_config.json
+│       ├── processor_config.json
+│       ├── preprocessor_config.json
+│       ├── special_tokens_map.json
+│       ├── chat_template.json
+│       └── README.md
+└── results/
+    └── fold0/
+        ├── metrics.json
+        ├── confusion_matrix.json
+        ├── progress.json
+        └── README.md
+```
 
-### Model Sources [optional]
+## Download
 
-<!-- Provide the basic links for the model. -->
+Run the download command from the GitHub repository root. It writes files to `checkpoints/fold0/`, which is the path expected by the evaluation examples.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+```bash
+pip install -U huggingface_hub
+hf download ljc-1222/LMOL-fbp5500 \
+  --repo-type dataset \
+  --include "checkpoints/fold0/*" \
+  --local-dir .
+```
 
-## Uses
+To download both the checkpoint and result JSON files:
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+```bash
+hf download ljc-1222/LMOL-fbp5500 \
+  --repo-type dataset \
+  --include "checkpoints/fold0/*" "results/fold0/*" \
+  --local-dir .
+```
 
-### Direct Use
+Python equivalent:
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```python
+from huggingface_hub import snapshot_download
 
-[More Information Needed]
+snapshot_download(
+    repo_id="ljc-1222/LMOL-fbp5500",
+    repo_type="dataset",
+    allow_patterns=["checkpoints/fold0/*"],
+    local_dir=".",
+)
+```
 
-### Downstream Use [optional]
+## Use With The GitHub Repository
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+After installing dependencies, preparing SCUT-FBP5500, and building fold0 pairs, run:
 
-[More Information Needed]
+```bash
+python scripts/evaluate_fold.py \
+  --config configs/lmol_scut_fbp5500.yaml \
+  --fold-index 0 \
+  --checkpoint checkpoints/fold0 \
+  --batch-size 8
+```
 
-### Out-of-Scope Use
+Full fold0 evaluation scores 374,000 target-reference pairs and can take many hours. Use `--limit 256` only for a checkpoint smoke test; it does not reproduce the reported metrics.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+## Fold0 Result
 
-[More Information Needed]
+Score metrics from `results/fold0/metrics.json`:
 
-## Bias, Risks, and Limitations
+| Split | PC ↑ | MAE ↓ | RMSE ↓ |
+| --- | ---: | ---: | ---: |
+| Fold 0 | 0.9568 | 0.1643 | 0.2130 |
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Pairwise metrics computed from `results/fold0/confusion_matrix.json` and `results/fold0/progress.json`:
 
-[More Information Needed]
+| Split | Accuracy ↑ | Macro Precision ↑ | Macro Recall ↑ | Macro F1 ↑ | Weighted F1 �� | Pairwise NLL ↓ |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| Fold 0 | 0.8649 | 0.8145 | 0.8354 | 0.8195 | 0.8723 | 0.3384 |
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.13.2
+Per-answer pairwise metrics:
+
+| Gold Label | Support | Predicted | Precision ↑ | Recall ↑ | F1 ↑ |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| `First.` | 120,836 | 110,502 | 0.9335 | 0.8537 | 0.8918 |
+| `Second.` | 194,770 | 185,020 | 0.9557 | 0.9079 | 0.9312 |
+| `Similar.` | 58,394 | 78,478 | 0.5541 | 0.7446 | 0.6354 |
+
+## Checkpoint Details
+
+- Base model: `llava-hf/llava-1.5-7b-hf`
+- Trainable modules: PEFT LoRA adapters on the language model and the full multimodal projector.
+- Frozen modules: CLIP visual encoder.
+- LoRA: `r=8`, `lora_alpha=32`, `lora_dropout=0.05`.
+- Training objective: `choice_token`.
+- Fold: deterministic SCUT-FBP5500 fold0 with `split_seed: 42`.
+
+The `projector.pt` file is required. Loading only the PEFT adapter is incomplete for this implementation.
+
+## Limitations And Terms
+
+- This repository contains a checkpoint package and result metadata only. It does not contain SCUT-FBP5500 images or labels.
+- Users must obtain SCUT-FBP5500 from the official source and comply with the dataset authors' terms.
+- The GitHub repository currently does not specify a project license. Reuse rights should be clarified before redistribution.
+- Facial beauty prediction is sensitive and subjective. Use the checkpoint only for research reproduction and with appropriate ethical review.
+
+## Citation
+
+```bibtex
+@inproceedings{liu2025large,
+  title = {Large Multimodal Model is a Better Comparator on Facial Beauty Prediction},
+  author = {Liu, Zhenyou and Liang, Xuefeng and Lin, Jian},
+  booktitle = {ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages = {1--5},
+  year = {2025},
+  doi = {10.1109/ICASSP49660.2025.10889943}
+}
+```

checkpoints/fold0/README.md

@@ -0,0 +1,27 @@
+# Fold0 Checkpoint Files
+
+This folder contains the fold0 LMOL checkpoint package used by the GitHub repository evaluation examples.
+
+Required files:
+
+- `adapter_config.json`
+- `adapter_model.safetensors`
+- `projector.pt`
+- `tokenizer.json`
+- `tokenizer_config.json`
+- `processor_config.json`
+- `preprocessor_config.json`
+- `special_tokens_map.json`
+- `chat_template.json`
+
+`projector.pt` is required because this implementation trains and saves the full LLaVA multimodal projector separately from the PEFT adapter.
+
+Use this folder as:
+
+```bash
+python scripts/evaluate_fold.py \
+  --config configs/lmol_scut_fbp5500.yaml \
+  --fold-index 0 \
+  --checkpoint checkpoints/fold0 \
+  --batch-size 8
+```

results/fold0/README.md

@@ -0,0 +1,9 @@
+# Fold 0 Result Artifacts
+
+These files are small release copies of the completed fold0 evaluation outputs.
+
+- `metrics.json`: score metrics copied from `outputs/evaluation/fold_0/metrics.json`.
+- `confusion_matrix.json`: pairwise confusion matrix copied from `outputs/evaluation/fold_0/confusion_matrix.json`.
+- `progress.json`: final completed progress payload copied from `outputs/evaluation/fold_0/progress.json`.
+
+The full generated predictions CSV is not duplicated here because `outputs/evaluation/fold_0/pairwise_predictions.csv` is about 45 MB and is a generated artifact.

results/fold0/confusion_matrix.json

@@ -0,0 +1,26 @@
+{
+  "confusion_matrix_rows_gold_cols_pred": {
+    "First.": [
+      103159,
+      282,
+      17395
+    ],
+    "Second.": [
+      341,
+      176829,
+      17600
+    ],
+    "Similar.": [
+      7002,
+      7909,
+      43483
+    ]
+  },
+  "count": 374000,
+  "labels": [
+    "First.",
+    "Second.",
+    "Similar."
+  ],
+  "pairwise_accuracy": 0.8648957219251336
+}

results/fold0/metrics.json

@@ -0,0 +1,6 @@
+{
+  "count": 1100,
+  "mae": 0.16428181454545454,
+  "pc": 0.9568051338710857,
+  "rmse": 0.21298467207160493
+}

results/fold0/progress.json

@@ -0,0 +1,24 @@
+{
+  "batch_size": 8,
+  "checkpoint": "outputs/lmol_scut_fbp5500/fold_0/last",
+  "confusion": "outputs/evaluation/fold_0/confusion_matrix.json",
+  "elapsed_sec": 68173.94125270844,
+  "eta_sec": 0.0,
+  "metrics": "outputs/evaluation/fold_0/metrics.json",
+  "pairs_csv": "artifacts/pairs/full/fold_0/test_reference_pairs.csv",
+  "pairs_per_sec": 5.485967117753246,
+  "pairwise_accuracy_running": 0.8648957219251336,
+  "pairwise_loss_running": 0.3383971469685508,
+  "percent": 100.0,
+  "phase": "complete",
+  "predictions": "outputs/evaluation/fold_0/pairwise_predictions.csv",
+  "processed_pairs": 374000,
+  "score_metrics": {
+    "count": 1100,
+    "mae": 0.16428181454545454,
+    "pc": 0.9568051338710857,
+    "rmse": 0.21298467207160493
+  },
+  "total_pairs": 374000,
+  "updated_at": "2026-06-07T08:38:00Z"
+}