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--- |
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license: apache-2.0 |
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language: |
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- zh |
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- en |
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metrics: |
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- bleu |
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base_model: |
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- Qwen/Qwen2.5-7B-Instruct |
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--- |
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[[Paper]](https://arxiv.org/abs/2407.17331) [[GitHub]](https://github.com/deepglint/unicom) |
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## Embodied Ability Evaluation: Performance in RoboVQA and OpenEQA |
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| | | MLCD <br> Embodied-7B | LLaVA <br> OneVision-7B | GPT-4v | RoboMamba | |
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:-- | :-- | :-: | :-: | :-: | :-: | |
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| RoboVQA | BLEU1 | <span style="color:red">73.16</span> | 38.12 | - | 54.9 | |
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| | BLEU2 | <span style="color:red">66.39</span> | 33.56 | - | 44.2 | |
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| | BLEU3 | <span style="color:red">60.61</span> | 31.76 | - | 39.5 | |
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| | BLEU4 | <span style="color:red">56.56</span> | 30.97 | - | 36.3 | |
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| OpenEQA | Object State Recognition | <span style="color:red">71.83</span> | - | 63.2 | - | |
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| | Object Recognition | <span style="color:red">49.46</span> | - | 43.4 | - | |
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| | Functional Reasoning | 54.38 | - | <span style="color:red">57.4</span> | - | |
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| | Spatial Understanding | <span style="color:red">48.64</span> | - | 33.6 | - | |
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| | Attribute Recognition | <span style="color:red">67.08</span> | - | 57.2 | - | |
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| | World Knowledge | <span style="color:red">53.87</span> | - | 50.7 | - | |
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| | Object Localization | <span style="color:red">43.06</span> | - | 42.0 | - | |
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## General Ability Evaluation: Comparison with LLaVA OneVision-7B and GPT-4 |
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| Dataset | Split | MLCD<br>Embodied-7B | LLaVA<br>OneVision-7B | GPT-4v | GPT-4o | |
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| :-- | :-: | :-: | :-: | :-: | :-: | |
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| A12D | test | 79.9 | 81.4 | 78.2 | 94.2 | |
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| ChartQA | test | 83.0 | 80.0 | 78.5 | 85.7 | |
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| DocVQA | test | 91.6 | 87.5 | 88.4 | 92.8 | |
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| InfoVQA | val | 73.9 | 70.7 | - | - | |
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| InfoVQA | test | 70.0 | 68.8 | - | - | |
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| MMMU | val | 47.3 | 48.8 | 56.8 | 69.1 | |
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| MMStar | test | 58.5 | 61.7 | 57.1 | 63.9 | |
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| OCRBench | - | 749.0 | 697.0 | 656.0 | 805.0 | |
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| RealWorldQA | test | 68.9 | 66.3 | 61.4 | 58.6 | |
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| SeedBench | image | 74.9 | 75.4 | 49.9 | 76.2 | |
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| MMbench | en-dev | 81.1 | 83.2 | 81.3 | 83.4 | |
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| MMbench | en-test | 80.1 | 80.8 | 75.0 | - | |
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| MME | test | 578/1603 | 418/1580 | 517/1409 | - | |
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## Usage |
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### A. Installation |
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```bash |
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git clone https://github.com/deepglint/unicom |
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cd unicom |
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# Upgrade pip and install necessary dependencies |
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pip install --upgrade pip |
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pip install -e ".[train]" |
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``` |
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### B. Inference |
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```bash |
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git clone https://github.com/deepglint/unicom |
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cd unicom |
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pip install --upgrade pip |
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pip install -e ".[train]" |
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pip install flash-attn --no-build-isolation |
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CUDA_VISIBLE_DEVICES=0 python infer.py --model_dir DeepGlint-AI/MLCD-Embodied-7B |
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# example: |
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# >> Enter 'exit' to end the conversation, 'reset' to clear the chat history. |
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# >> Enter image file paths (comma-separated): ./asserts/logo.png |
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# >> User: <image>What kind of animal is it in this picture? |
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# >> Assistant: The image features a stylized representation of a cat, characterized by its vibrant and abstract depiction. |
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# >> User: What color is this cat? |
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# >> Assistant: The cat in the image is primarily white with blue, orange and pink accents, creating a visually appealing and unique appearance. |
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# >> User: <image>请你介绍一下这个图片 |
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# >> Assistant: 这是一幅充满创意的猫头艺术作品。它采用了多色渐变和抽象风格，将猫的头部描绘成一个充满活力和色彩的视觉冲击。猫的眼睛用金色渲染，显得非常有神采， |
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# 而粉色的鼻子则增添了一丝可爱感。整体设计融合了现代艺术与传统猫头图案，创造出一种既独特又引人入胜的视觉效果。。 |
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``` |
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### C. Evaluation for Embodied Ability |
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#### Step 1 |
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Download raw data following [OpenEQA](https://github.com/facebookresearch/open-eqa/tree/main/data) and [RoboVQA](https://console.cloud.google.com/storage/browser/gdm-robovqa)(val part) |
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#### Step 2 |
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Converting raw data into the format required for model evaluation. |
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```bash |
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# convert OpenEQA benchmark. Note: replace the paths with your own. |
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python llava/benchmark/make_openeqa_bmk.py |
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# convert RoboVQA benchmark. Note: replace the paths with your own. |
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python llava/benchmark/make_robovqa_bmk.py |
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``` |
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#### Step 3 |
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Make sure that your top-level directory structure should look like this: |
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``` |
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|--/path/to/your/benchmarks |
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| |--OpenEQA |
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| | |--openeqa_scannet.parquet |
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| | |--openeqa_hm3d.parquet |
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| |--RoboVQA |
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| |--robovqa.parquet |
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|--/path/to/your/images |
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|--openeqa_val |
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| |--scannet-v0 |
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| | |--002-scannet-scene0709_00 |
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| | |--xxx-scannet-scenexxxx_xx |
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| |--hm3d-v0 |
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| |--000-hm3d-BFRyYbPCCPE |
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| |--xxx-hm3d-xxxxxxxxxxx |
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|--robovqa_val |
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|--robovqa_221911 |
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|--robovqa_xxxxxx |
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``` |
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#### Step 4 |
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Run script for evaluation |
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```bash |
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# Note: replace 'YOUR_API_KEY', 'YOUR_ENDPOINT', 'bmk_root', 'image_folder' with your own. |
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bash scripts/eval/eval_robo.sh /path/to/your/model |
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``` |
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### D. Evaluation for General Ability |
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Install the evaluation tool and execute the evaluation script: |
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```bash |
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pip install lmms-eval==0.2.0 |
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PYTHONPATH=./ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m accelerate.commands.launch \ |
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--main_process_port=12444 \ |
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--num_processes=8 \ |
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-m lmms_eval \ |
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--model llava \ |
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--model_args pretrained=DeepGlint-AI/MLCD-Embodied-7B,conv_template=qwen_1_5 \ |
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--tasks mme \ |
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--batch_size 1 \ |
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--log_samples \ |
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--log_samples_suffix mlcd \ |
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--output_path ./eval_log/ |
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``` |
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We would like to express our gratitude to [Huajie Tan](https://huggingface.co/tanhuajie2001), [Yumeng Wang](https://huggingface.co/devymex), [Yin Xie](https://huggingface.co/Yin-Xie) for his significant contributions to the experimental validation in MLLMs. |
