README.md

---

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.