Skip to content

Latest commit

 

History

History
271 lines (216 loc) · 8.07 KB

EVAL.md

File metadata and controls

271 lines (216 loc) · 8.07 KB
Raw

Data preparation

  • Following LLaVA's instructions. You MUST first download eval.zip.
  • It contains custom annotations, scripts, and the prediction files with LLaVA v1.5. Extract to eval. This also provides a general structure for all datasets.

After downloading all of them, organize the data as follows in eval.

eval
├── gqa
│   ├── answers
│   ├── data
│   └── llava_gqa_testdev_balanced.jsonl
├── llava-bench-in-the-wild
│   ├── answers
│   ├── answers_gpt4.jsonl
│   ├── bard_0718.jsonl
│   ├── bing_chat_0629.jsonl
│   ├── context.jsonl
│   ├── images
│   ├── questions.jsonl
│   ├── README.md
│   └── reviews
├── mmbench
│   ├── answers
│   ├── answers_upload
│   ├── mmbench_dev_20230712.tsv
│   └── mmbench_dev_en_20231003.tsv
├── MME
│   ├── answers
│   ├── convert_answer_to_mme.py
│   └── llava_mme.jsonl
├── mm-vet
│   ├── answers
│   ├── bard_set.json
│   ├── convert_answers.py
│   ├── images
│   ├── llava-mm-vet.jsonl
│   ├── mm-vet.json
│   └── results
├── pope
│   ├── answers
│   ├── coco
│   ├── llava_pope_test.jsonl
│   └── val2014
├── scienceqa
│   ├── answers
│   ├── images
│   ├── llava_test_CQM-A.json
│   ├── pid_splits.json
│   └── problems.json
├── seed_bench
│   ├── answers
│   ├── answers_upload
│   ├── extract_video_frames.py
│   └── llava-seed-bench.jsonl
├── textvqa
│   ├── answers
│   ├── llava_textvqa_val_v051_ocr.jsonl
│   ├── TextVQA_0.5.1_val.json
│   └── train_images
├── vizwiz
│   ├── answers
│   ├── answers_upload
│   ├── llava_test.jsonl
│   ├── test
│   ├── test.json
│   ├── train.json
│   └── val.json
└── vqav2
    ├── answers
    ├── answers_upload
    ├── llava_vqav2_mscoco_test2015.jsonl
    ├── llava_vqav2_mscoco_test-dev2015.jsonl
    └── test2015

Validating

Our image validation code comes from LLaVA, thanks for their contribution!

You can refer to the official repository for validation, but we also provide off-the-shelf scripts.

VQAv2

  1. Download test2015 and put it under eval/vqav2.
  2. Multi-GPU inference.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1/eval/llava/vqav2.sh

MoE-based model

bash scripts/v1/eval/moe_llava/vqav2.sh
  1. Submit the results to the evaluation server: eval/vqav2/answers_upload.

GQA

  1. Download the data following the official instructions here and put under eval/gqa/data.
  2. Multi-GPU inference

LLaVA-based model

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1/eval/llava/gqa.sh

MoE-based model

bash scripts/v1/eval/moe_llava/gqa.sh

VisWiz

  1. Download test.json and extract test.zip to test. Put them under eval/vizwiz.
  2. Single-GPU inference.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0 bash scripts/v1/eval/moe_llava/vizwiz.sh

MoE-based model

bash scripts/v1/eval/moe_llava/vizwiz.sh
  1. Submit the results to the evaluation server: eval/vizwiz/answers_upload.

ScienceQA

  1. Under eval/scienceqa, download images, pid_splits.json, problems.json from the data/scienceqa folder of the ScienceQA repo.
  2. Single-GPU inference and evaluate.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0 bash scripts/v1/eval/moe_llava/sqa.sh

MoE-based model

bash scripts/v1/eval/moe_llava/sqa.sh

TextVQA

  1. Download TextVQA_0.5.1_val.json and images and extract to eval/textvqa.
  2. Single-GPU inference and evaluate.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0 bash scripts/v1/eval/moe_llava/textvqa.sh

MoE-based model

bash scripts/v1/eval/moe_llava/textvqa.sh

POPE

  1. Download coco from POPE and put under eval/pope.
  2. Single-GPU inference and evaluate.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0 bash scripts/v1/eval/moe_llava/pope.sh

MoE-based model

bash scripts/v1/eval/moe_llava/pope.sh

MME

  1. Download the data following the official instructions here.
  2. Downloaded images to MME_Benchmark_release_version.
  3. Put the official eval_tool and MME_Benchmark_release_version under eval/MME.
  4. Single-GPU inference and evaluate.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0 bash scripts/v1/eval/llava/mme.sh

MoE-based model

bash scripts/v1/eval/moe_llava/mme.sh

MMBench

  1. Download mmbench_dev_20230712.tsv and put under eval/mmbench.
  2. Single-GPU inference.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0 bash scripts/v1/eval/llava/mmbench.sh

MoE-based model

bash scripts/v1/eval/moe_llava/mmbench.sh
  1. Submit the results to the evaluation server: eval/mmbench/answers_upload/mmbench_dev_20230712.

MMBench-CN

  1. Download mmbench_dev_cn_20231003.tsv and put under eval/mmbench.
  2. Single-GPU inference.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0 bash scripts/v1/eval/llava/mmbench_cn.sh

MoE-based model

bash scripts/v1/eval/moe_llava/mmbench_cn.sh
  1. Submit the results to the evaluation server: eval/mmbench/answers_upload/mmbench_dev_cn_20231003.

SEED-Bench

  1. Following the official instructions to download the images and the videos. Put images under eval/seed_bench/SEED-Bench-image.
  2. Extract the video frame in the middle from the downloaded videos, and put them under eval/seed_bench/SEED-Bench-video-image.
  3. Multiple-GPU inference and evaluate.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1/eval/llava/seed.sh

MoE-based model

bash scripts/v1/eval/moe_llava/seed.sh
  1. Optionally, submit the results to the leaderboard: eval/seed_bench/answers_upload using the official jupyter notebook.

LLaVA-Bench-in-the-Wild

  1. Extract contents of llava-bench-in-the-wild to eval/llava-bench-in-the-wild.
  2. Single-GPU inference and evaluate.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0 bash scripts/v1/eval/moe_llava/llavabench.sh

MoE-based model

bash scripts/v1/eval/moe_llava/llavabench.sh

MM-Vet

  1. Extract mm-vet.zip to eval/mmvet.
  2. Single-GPU inference.

LLaVA-based model

CUDA_VISIBLE_DEVICES=0 bash scripts/v1/eval/moe_llava/mmvet.sh

MoE-based model

bash scripts/v1/eval/moe_llava/mmvet.sh