docs/Evaluation.md

# Evaluation

In LLaVA-1.5, we evaluate models on a diverse set of 12 benchmarks. To ensure the reproducibility, we evaluate the models with greedy decoding. We do not evaluate using beam search to make the inference process consistent with the chat demo of real-time outputs.

Currently, we mostly utilize the official toolkit or server for the evaluation.

## Evaluate on Custom Datasets

You can evaluate LLaVA on your custom datasets by converting your dataset to LLaVA's jsonl format, and evaluate using [`model_vqa.py`](https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/model_vqa.py).

Below we provide a general guideline for evaluating datasets with some common formats.

1. Short-answer (e.g. VQAv2, MME).

```
<question>
Answer the question using a single word or phrase.
```

2. Option-only for multiple-choice (e.g. MMBench, SEED-Bench).

```
<question>
A. <option_1>
B. <option_2>
C. <option_3>
D. <option_4>
Answer with the option's letter from the given choices directly.
```

3. Natural QA (e.g. LLaVA-Bench, MM-Vet).

No postprocessing is needed.

## Scripts

Before preparing task-specific data, download [eval.zip](https://drive.google.com/file/d/1atZSBBrAX54yYpxtVVW33zFvcnaHeFPy/view?usp=sharing). It contains custom annotations, scripts, and the prediction files with LLaVA v1.5. Extract to `./playground/data/eval`. This also provides a general structure for all datasets.

### VQAv2

1. Download [`test2015`](http://images.cocodataset.org/zips/test2015.zip) and put it under `./playground/data/eval/vqav2`.
2. Multi-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/vqav2.sh
```
3. Submit the results to the evaluation server: `./playground/data/eval/vqav2/answers_upload`.

### GQA

1. Download the data following the official instructions [here](https://cs.stanford.edu/people/dorarad/gqa/download.html) and put under `./playground/data/eval/gqa/data`.
2. Multi-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/gqa.sh
```

### VisWiz

1. Download [`test.json`](https://vizwiz.cs.colorado.edu/VizWiz_final/vqa_data/Annotations.zip) and extract [`test.zip`](https://vizwiz.cs.colorado.edu/VizWiz_final/images/test.zip) to `test`. Put them under `./playground/data/eval/vizwiz`.
2. Single-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/vizwiz.sh
```
3. Submit the results to the evaluation server: `./playground/data/eval/vizwiz/answers_upload`.

### ScienceQA

1. Under `./playground/data/eval/scienceqa`, download `images`, `pid_splits.json`, `problems.json` from the `data/scienceqa` folder of the ScienceQA [repo](https://github.com/lupantech/ScienceQA).
2. Single-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/sqa.sh
```

### TextVQA

1. Download [`TextVQA_0.5.1_val.json`](https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_val.json) and [images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip) and extract to `./playground/data/eval/textvqa`.
2. Single-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/textvqa.sh
```

### POPE

1. Download `coco` from [POPE](https://github.com/AoiDragon/POPE/tree/e3e39262c85a6a83f26cf5094022a782cb0df58d/output/coco) and put under `./playground/data/eval/pope`.
2. Single-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/pope.sh
```

### MME

1. Download the data following the official instructions [here](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation).
2. Downloaded images to `MME_Benchmark_release_version`.
3. put the official `eval_tool` and `MME_Benchmark_release_version` under `./playground/data/eval/MME`.
4. Single-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mme.sh
```

### MMBench

1. Download [`mmbench_dev_20230712.tsv`](https://download.openmmlab.com/mmclassification/datasets/mmbench/mmbench_dev_20230712.tsv) and put under `./playground/data/eval/mmbench`.
2. Single-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mmbench.sh
```
3. Submit the results to the evaluation server: `./playground/data/eval/mmbench/answers_upload/mmbench_dev_20230712`.

### MMBench-CN

1. Download [`mmbench_dev_cn_20231003.tsv`](https://download.openmmlab.com/mmclassification/datasets/mmbench/mmbench_dev_en_20231003.tsv) and put under `./playground/data/eval/mmbench`.
2. Single-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mmbench_cn.sh
```
3. Submit the results to the evaluation server: `./playground/data/eval/mmbench/answers_upload/mmbench_dev_cn_20231003`.

### SEED-Bench

1. Following the official [instructions](https://github.com/AILab-CVC/SEED-Bench/blob/main/DATASET.md) to download the images and the videos. Put images under `./playground/data/eval/seed_bench/SEED-Bench-image`.
2. Extract the video frame in the middle from the downloaded videos, and put them under `./playground/data/eval/seed_bench/SEED-Bench-video-image`. We provide our script `extract_video_frames.py` modified from the official one.
3. Multiple-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/seed.sh
```
4. Optionally, submit the results to the leaderboard: `./playground/data/eval/seed_bench/answers_upload` using the official jupyter notebook.

### LLaVA-Bench-in-the-Wild

1. Extract contents of [`llava-bench-in-the-wild`](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild) to `./playground/data/eval/llava-bench-in-the-wild`.
2. Single-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/llavabench.sh
```

### MM-Vet

1. Extract [`mm-vet.zip`](https://github.com/yuweihao/MM-Vet/releases/download/v1/mm-vet.zip) to `./playground/data/eval/mmvet`.
2. Single-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mmvet.sh
```
3. Evaluate the predictions in `./playground/data/eval/mmvet/results` using the official jupyter notebook.