Update README.md

README.md

@@ -7,4 +7,116 @@ language:
 tags:
 - uav
 pretty_name: mm-uavbench
----
+---
+# MM-UAVBench
+
+A comprehensive multimodal benchmark designed to evaluate the perception, cognition, and planning abilities of Multimodal Large Language Models (MLLMs) in low-altitude UAV scenarios.
+
+## 📚 Dataset Overview
+
+MM-UAVBench focuses on assessing MLLMs' performance in UAV-specific low-altitude scenarios, with three core characteristics:
+
+### Key Features
+
+1. **Comprehensive Task Design**
+
+    19 tasks across 3 capability dimensions (perception/cognition/planning), incorporating UAV-specific considerations – specifically multi-level cognition (object/scene/event) and planning for both aerial and ground agents.
+
+2. **Diverse Real-World Scenarios**
+* 1,549 real-world UAV video clips
+* 2,873 high-resolution UAV images (avg. resolution: 1622 x 1033)
+* Collected from diverse real-world low-altitude scenarios (urban/suburban/rural)
+
+3. **High-Quality Annotations**
+* 5,702 multiple-choice QA pairs in total
+* 16 tasks with manual human annotations
+* 3 additional tasks via rule-based transformation of manual labels
+
+## 🎯 Dataset Structure
+
+```plaintext
+MM-UAVBench/
+├── images/          
+│   ├── annotated/   # Annotated images (used for official benchmark evaluation)
+│   └── raw/         # Unannotated raw UAV images (open-sourced for custom annotation)
+├── tasks/           # QA annotations 
+├── tools/
+│   └── render_annotated.py  # Script to render labels on raw images
+│   └── util.py              # Visualization tools
+└── README.md                # Dataset usage guide
+```
+### Important Notes on Image Files
+* **Evaluation Usage**: The benchmark evaluation is conducted using annotated images in `images/annotated/`.
+* **Raw Images for Custom Annotation**: We also open-source unannotated raw UAV images in `images/raw/`. You can refer to the `tools/render_annotated.py` script to render custom labels on these raw images.
+
+## 🚀 Quick Start
+### Evaluate MLLMs on MM-UAVBench
+MM-UAVBench is fully compatible with [VLMEvalKit](https://github.com/open-compass/VLMEvalKit):
+#### Step 1: Install Dependencies
+
+```bash
+git clone https://github.com/MM-UAVBench/MM-UAVBench.git 
+cd MM-UAVBench
+git clone https://github.com/open-compass/VLMEvalKit.git
+cd VLMEvalKit
+pip install -e .
+```
+
+#### Step 2: Configure Evaluation Dataset
+Copy the dataset file to the VLMEvalKit directory:
+```bash
+cp ~/MM-UAVBench/mmuavbench.py ~/MM-UAVBench/VLMEvalKit/vlmeval/dataset
+```
+Edit `~/MM-UAVBench/VLMEvalKit/vlmeval/dataset/__init__.py` and add the following content:
+```python
+from.mmuavbench import MMUAVBench_Image, MMUAVBench_Video
+
+IMAGE_DATASET = [
+    # Existing datasets
+    MMUAVBench_Image,
+]
+
+VIDEO_DATASET = [
+    # Existing datasets
+    MMUAVBench_Video,
+]
+```
+
+#### Step 3: Download Dataset
+Download the dataset from [huggingface](https://huggingface.co/datasets/daisq/MM-UAVBench) and put it in `~/MM-UAVBench/data`. 
+
+Set the dataset path in `~/MM-UAVBench/VLMEvalKit/.env`：
+```
+LMUData="~/MM-UAVBench/data"
+```
+#### Step 4: Run Evaluation
+Modify the model checkpoint path in `~/MM-UAVBench/VLMEvalKit/vlmeval/config.py` to your target model path.
+
+Run the evaluation command:
+```bash
+python run.py \
+    --data MMUAVBench_Image MMUAVBench_Video \
+    --model Qwen3-VL-8B-Instruct \
+    --mode all \
+    --work-dir ~/MM-UAVBench/eval_results \
+    --verbose
+```
+
+### Render Custom Annotations on Raw Images
+To generate annotated images from raw files (using our script):
+```bash
+# 1. Set your MM-UAVBench root directory in render_annotated.py
+# 2. Run the annotation rendering script
+python tools/render_annotated.py
+```
+## 📖 Citation
+ If you find MM-UAVBench useful in your research tasks or applications, please consider to give **star⭐** and kindly cite:
+```
+@article{dai2025mm,
+  title={MM-UAVBench: How Well Do Multimodal Large Language Models See, Think, and Plan in Low-Altitude UAV Scenarios?},
+  author={Dai, Shiqi and Ma, Zizhi and Luo, Zhicong and Yang, Xuesong and Huang, Yibin and Zhang, Wanyue and Chen, Chi and Guo, Zonghao and Xu, Wang and Sun, Yufei and others},
+  journal={arXiv preprint arXiv:2512.23219},
+  year={2025}，
+  url={https://arxiv.org/abs/2512.23219}
+}
+```