Embodied Ability Evaluation: Performance in RoboVQA and OpenEQA
| MLCD Embodied-7B | LLaVA OneVision-7B | GPT-4v | RoboMamba | ||
|---|---|---|---|---|---|
| RoboVQA | BLEU1 | 73.16 | 38.12 | - | 54.9 |
| BLEU2 | 66.39 | 33.56 | - | 44.2 | |
| BLEU3 | 60.61 | 31.76 | - | 39.5 | |
| BLEU4 | 56.56 | 30.97 | - | 36.3 | |
| OpenEQA | Object State Recognition | 71.83 | - | 63.2 | - |
| Object Recognition | 49.46 | - | 43.4 | - | |
| Functional Reasoning | 54.38 | - | 57.4 | - | |
| Spatial Understanding | 48.64 | - | 33.6 | - | |
| Attribute Recognition | 67.08 | - | 57.2 | - | |
| World Knowledge | 53.87 | - | 50.7 | - | |
| Object Localization | 43.06 | - | 42.0 | - |
General Ability Evaluation: Comparison with LLaVA OneVision-7B and GPT-4
| Dataset | Split | MLCDEmbodied-7B | LLaVAOneVision-7B | GPT-4v | GPT-4o |
|---|---|---|---|---|---|
| A12D | test | 79.9 | 81.4 | 78.2 | 94.2 |
| ChartQA | test | 83.0 | 80.0 | 78.5 | 85.7 |
| DocVQA | test | 91.6 | 87.5 | 88.4 | 92.8 |
| InfoVQA | val | 73.9 | 70.7 | - | - |
| InfoVQA | test | 70.0 | 68.8 | - | - |
| MMMU | val | 47.3 | 48.8 | 56.8 | 69.1 |
| MMStar | test | 58.5 | 61.7 | 57.1 | 63.9 |
| OCRBench | - | 749.0 | 697.0 | 656.0 | 805.0 |
| RealWorldQA | test | 68.9 | 66.3 | 61.4 | 58.6 |
| SeedBench | image | 74.9 | 75.4 | 49.9 | 76.2 |
| MMbench | en-dev | 81.1 | 83.2 | 81.3 | 83.4 |
| MMbench | en-test | 80.1 | 80.8 | 75.0 | - |
| MME | test | 578/1603 | 418/1580 | 517/1409 | - |
Usage
A. Installation
git clone https://github.com/deepglint/unicom
cd unicom/mlcd_vl
docker build -t train_mlcd_llava .
docker run --gpus all \
-v /vlm:/vlm \
-v /mnt:/mnt \
-v $(pwd):/workspace \
--rm \
-w /workspace \
--shm-size=64g -it train_mlcd_llava bash
pip install flash-attn==2.3.3 --no-build-isolation
B. Inference
CUDA_VISIBLE_DEVICES=0 python infer_mlcd_emboided.py --model_dir DeepGlint-AI/MLCD-Embodied-7B
# example:
# >> Enter 'exit' to end the conversation, 'reset' to clear the chat history.
# >> Enter image file paths (comma-separated): ../_static/images/logo.png
# >> User: <image>What kind of animal is it in this picture?
# >> Assistant: The image features a stylized representation of a cat, characterized by its vibrant and abstract depiction.
# >> User: What color is this cat?
# >> Assistant: The cat in the image is primarily white with blue, orange and pink accents, creating a visually appealing and unique appearance.
C. Evaluation for Embodied Ability
Step 1
Download raw data following OpenEQA and RoboVQA(val part)
Step 2
Converting raw data into the format required for model evaluation.
# convert OpenEQA benchmark. Note: replace the paths with your own.
python llava/benchmark/make_openeqa_bmk.py
# convert RoboVQA benchmark. Note: replace the paths with your own.
python llava/benchmark/make_robovqa_bmk.py
Step 3
Make sure that your top-level directory structure should look like this:
|--/path/to/your/benchmarks
| |--OpenEQA
| | |--openeqa_scannet.parquet
| | |--openeqa_hm3d.parquet
| |--RoboVQA
| |--robovqa.parquet
|--/path/to/your/images
|--openeqa_val
| |--scannet-v0
| | |--002-scannet-scene0709_00
| | |--xxx-scannet-scenexxxx_xx
| |--hm3d-v0
| |--000-hm3d-BFRyYbPCCPE
| |--xxx-hm3d-xxxxxxxxxxx
|--robovqa_val
|--robovqa_221911
|--robovqa_xxxxxx
Step 4
Run script for evaluation
# Note: replace 'YOUR_API_KEY', 'YOUR_ENDPOINT', 'bmk_root', 'image_folder' with your own.
bash scripts/eval/eval_robo.sh /path/to/your/model
D. Evaluation for General Ability
Install the evaluation tool and execute the evaluation script:
pip install lmms-eval==0.2.0
PYTHONPATH=./ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m accelerate.commands.launch \
--main_process_port=12444 \
--num_processes=8 \
-m lmms_eval \
--model llava \
--model_args pretrained=DeepGlint-AI/MLCD-Embodied-7B,conv_template=qwen_1_5 \
--tasks mme \
--batch_size 1 \
--log_samples \
--log_samples_suffix mlcd \
--output_path ./eval_log/
We would like to express our gratitude to Huajie Tan, Yumeng Wang, Yin Xie for his significant contributions to the experimental validation in MLLMs.