---
library_name: peft
license: other
base_model: Qwen/Qwen3-VL-8B-Instruct
pipeline_tag: image-text-to-text
tags:
- qwen3-vl
- vision-language
- portrait-aesthetics
- aesthetics-evaluation
- lora
- peft
- llama-factory
---

# Portrait Composition Aesthetics Evaluation

This project provides an inference and fusion pipeline for portrait composition aesthetics evaluation. It converts the official test set into multi-prompt prediction inputs, runs prediction with Stage2 LoRA adapters, and fuses multiple prediction files into the final `track_1_test.json`.

## 1. Project Structure

```text
project_root/
  prepare_test_prompt_v2.py          # Build multi-prompt test inputs
  convert_to_answer.py               # Fuse multi-path prediction results
  train_track1_stage1_qwen3vl8b_v1.yaml
  train_track1_stage2_qwen3vl8b_v1.yaml
  qwen3vl8b_r1stage2_seed_ensemble_fast_20260511_112825/
    seed20260511_stage2/             # Stage2 adapter 1
    seed20260512_stage2/             # Stage2 adapter 2
  datasets/
    images/                          # Test images
    original_annotations/
      track_1_test.json              # Official test template
  outputs/
    predictions/                     # Model prediction outputs
    submissions/answers/             # Final submission file
```

For path conventions, see `REPRODUCIBLE_PATHS.md`.

## 2. Environment

Use an environment that supports Qwen3-VL and LoRA inference. LLaMA-Factory is recommended for prediction.

Basic dependencies:

```bash
pip install torch transformers peft accelerate
```

If you use LLaMA-Factory, install it first and make sure `llamafactory-cli` is available.

Base model:

```text
Qwen/Qwen3-VL-8B-Instruct
```

## 3. Prepare Test Data

Place the official test file at:

```text
datasets/original_annotations/track_1_test.json
```

Place test images under:

```text
datasets/images/
```

Generate multi-prompt test inputs:

```bash
python prepare_test_prompt_v2.py \
  --input_json datasets/original_annotations/track_1_test.json \
  --image_dir datasets/images \
  --output_json outputs/predictions/track1_test_convert.json \
  --prompt_variants v1,v2,v3,v4,v6,v7,v8
```

This command generates:

```text
outputs/predictions/track1_test_convert_v1.json
outputs/predictions/track1_test_convert_v2.json
outputs/predictions/track1_test_convert_v3.json
outputs/predictions/track1_test_convert_v4.json
outputs/predictions/track1_test_convert_v6.json
outputs/predictions/track1_test_convert_v7.json
outputs/predictions/track1_test_convert_v8.json
```

## 4. Run Multi-Path Prediction

Use the Stage2 adapters to run prediction on the generated prompt variants:

```text
qwen3vl8b_r1stage2_seed_ensemble_fast_20260511_112825/seed20260511_stage2
qwen3vl8b_r1stage2_seed_ensemble_fast_20260511_112825/seed20260512_stage2
```

If you use LLaMA-Factory, register each test JSON in `dataset_info.json`. Example:

```json
{
  "track1_test_v1": {
    "file_name": "outputs/predictions/track1_test_convert_v1.json",
    "formatting": "sharegpt",
    "columns": {
      "messages": "messages",
      "images": "images"
    }
  }
}
```

Example prediction config:

```yaml
model_name_or_path: Qwen/Qwen3-VL-8B-Instruct
adapter_name_or_path: qwen3vl8b_r1stage2_seed_ensemble_fast_20260511_112825/seed20260511_stage2
template: qwen3_vl_nothink
stage: sft
finetuning_type: lora

dataset_dir: .
eval_dataset: track1_test_v1
do_predict: true
predict_with_generate: true
cutoff_len: 4096
image_max_pixels: 786432
per_device_eval_batch_size: 8
max_new_tokens: 384
do_sample: false
top_p: 1.0
repetition_penalty: 1.02
bf16: true
flash_attn: auto

output_dir: outputs/predictions/seed20260511_v1
```

Run prediction:

```bash
llamafactory-cli train predict_seed20260511_v1.yaml
```

Each prediction run should produce:

```text
outputs/predictions/<run_name>/generated_predictions.jsonl
```

## 5. Fuse Prediction Results

Copy or symlink the official test template to:

```text
outputs/predictions/track_1_test.json
```

Fuse prediction results:

```bash
python convert_to_answer.py \
  --template_json outputs/predictions/track_1_test.json \
  --predictions_jsonl \
    outputs/predictions/seed20260511_v1/generated_predictions.jsonl \
    outputs/predictions/seed20260511_v2/generated_predictions.jsonl \
    outputs/predictions/seed20260511_v3/generated_predictions.jsonl \
    outputs/predictions/seed20260511_v4/generated_predictions.jsonl \
    outputs/predictions/seed20260511_v6/generated_predictions.jsonl \
    outputs/predictions/seed20260511_v7/generated_predictions.jsonl \
    outputs/predictions/seed20260511_v8/generated_predictions.jsonl \
  --weights 1 1 1 1 1 1 1 \
  --best_index 0 \
  --total_score_fusion mean \
  --output_json outputs/submissions/answers/track_1_test.json
```

To fuse results from both Stage2 adapters, add all corresponding `generated_predictions.jsonl` files to `--predictions_jsonl` and provide the same number of values in `--weights`.

## 6. Output

The final result is:

```text
outputs/submissions/answers/track_1_test.json
```

The file preserves the official template structure and fills in the fused:

- `criteria.level`
- `total_score`
- `answer`

## 7. Notes

- All paths are relative to the project root.
- Image paths use the format `datasets/images/<image_name>.jpg`.
- `convert_to_answer.py` supports multi-path prediction fusion, weighted answer voting, majority voting for levels, and mean fusion for `total_score`.
- If the model output is not strict JSON, the fusion script attempts to extract the JSON snippet and answer field from text.