Update README.md

README.md

@@ -1,3 +1,145 @@
----
-license: apache-2.0
----
+---
+task_categories:
+- visual-question-answering
+language:
+- en
+tags:
+- Vision
+- remote-sensing
+configs:
+- config_name: CLRS
+  data_files:
+  - split: test
+    path: clrs/data-*.arrow
+- config_name: UC_Merced
+  data_files:
+  - split: test
+    path: UCMerced/data-*.arrow
+- config_name: FloodNet
+  data_files:
+  - split: test
+    path: floodnet/data-*.arrow
+- config_name: NWPU-Captions
+  data_files:
+  - split: test
+    path: NWPU/data-*.arrow
+---
+
+# Adapting Multimodal Large Language Models to Domains via Post-Training
+
+This repos contains the **remote sensing visual instruction tasks for evaluating MLLMs** in our paper: [On Domain-Specific Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930).
+
+The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains)
+
+## 1. Download Data  
+You can load datasets using the `datasets` library:  
+```python
+from datasets import load_dataset
+
+# Choose the task name from the list of available tasks
+task_name = 'CLRS'  # Options: 'CLRS', 'UC_Merced', 'FloodNet', 'NWPU-Captions'
+
+# Load the dataset for the chosen task
+data = load_dataset('AdaptLLM/remote-sensing-VQA-benchmark', task_name, split='test')
+
+print(list(data)[0])
+```
+
+The mapping between category names and indices for 'CLRS', 'UC_Merced' is: 
+
+```python3
+# CLRS
+label_to_name_map = {'0': 'agricultural', '1': 'airplane', '2': 'baseball diamond', '3': 'beach', '4': 'buildings',
+          '5': 'chaparral', '6': 'dense residential', '7': 'forest', '8': 'freeway', '9': 'golf course', '10': 'harbor', '11': 'intersection', '12': 'medium residential', '13': 'mobile home park', '14': 'overpass', '15': 'parking lot', '16': 'river',
+          '17': 'runway', '18': 'sparse residential', '19': 'storage tanks', '20': 'tennis court'}
+
+# UC_Merced
+label_to_name_map = {'0': 'agricultural', '1': 'airplane', '2': 'baseball diamond', '3': 'beach', '4': 'buildings',
+          '5': 'chaparral', '6': 'dense residential', '7': 'forest', '8': 'freeway', '9': 'golf course', '10': 'harbor', '11': 'intersection', '12': 'medium residential', '13': 'mobile home park', '14': 'overpass', '15': 'parking lot', '16': 'river',
+          '17': 'runway', '18': 'sparse residential', '19': 'storage tanks', '20': 'tennis court'}
+```
+
+## 2. Evaluate Any MLLM Compatible with vLLM on the Food Benchmarks
+
+We provide a guide to directly evaluate MLLMs such as LLaVA-v1.6 ([open-source version](https://huggingface.co/Lin-Chen/open-llava-next-llama3-8b)), Qwen2-VL-Instruct, and Llama-3.2-Vision-Instruct.  
+To evaluate other MLLMs, refer to [this guide](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_vision_language.py) for modifying the `BaseTask` class in the [vllm_inference/utils/task.py](https://github.com/bigai-ai/QA-Synthesizer/blob/main/vllm_inference/utils/task.py) file.  
+Feel free reach out to us for assistance!
+
+**The dataset loading script is embedded in the inference code, so you can directly run the following commands to evaluate MLLMs.**  
+
+### 1) Setup
+
+Install vLLM using `pip` or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source).  
+
+As recommended in the official vLLM documentation, install vLLM in a **fresh new** conda environment:
+
+```bash
+conda create -n vllm python=3.10 -y
+conda activate vllm
+pip install vllm  # Ensure vllm>=0.6.2 for compatibility with Llama-3.2. If Llama-3.2 is not used, vllm==0.6.1 is sufficient.
+```
+
+Clone the repository and navigate to the inference directory:
+
+```bash
+git clone https://github.com/bigai-ai/QA-Synthesizer.git
+cd QA-Synthesizer/vllm_inference
+RESULTS_DIR=./eval_results  # Directory for saving evaluation scores
+```
+
+### 2) Evaluate
+
+Run the following commands:
+
+```bash
+# Specify the domain: choose from ['remote-sensing', 'CLRS', 'UC_Merced', 'FloodNet', 'NWPU-Captions']
+# 'remote-sensing' runs inference on all food tasks; others run on individual tasks.
+DOMAIN='remote-sensing'
+
+# Specify the model type: choose from ['llava', 'qwen2_vl', 'mllama']
+# For LLaVA-v1.6, Qwen2-VL, and Llama-3.2-Vision-Instruct, respectively.
+MODEL_TYPE='qwen2_vl'
+
+# Set the model repository ID on Hugging Face. Examples:
+# "Qwen/Qwen2-VL-2B-Instruct", "AdaptLLM/remote-sensing-Qwen2-VL-2B-Instruct" for MLLMs based on Qwen2-VL-Instruct.
+# "meta-llama/Llama-3.2-11B-Vision-Instruct", "AdaptLLM/remote-sensing-Llama-3.2-11B-Vision-Instruct" for MLLMs based on Llama-3.2-Vision-Instruct.
+# "AdaptLLM/remote-sensing-LLaVA-NeXT-Llama3-8B" for MLLMs based on LLaVA-v1.6.
+MODEL=AdaptLLM/remote-sensing-Qwen2-VL-2B-Instruct
+
+# Set the directory for saving model prediction outputs:
+OUTPUT_DIR=./output/AdaMLLM-remote-sensing-Qwen-2B_${DOMAIN}
+
+# Run inference with data parallelism; adjust CUDA devices as needed:
+CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' bash run_inference.sh ${MODEL} ${DOMAIN} ${MODEL_TYPE} ${OUTPUT_DIR} ${RESULTS_DIR}
+```
+
+Detailed scripts to reproduce our results are in [Evaluation.md](https://github.com/bigai-ai/QA-Synthesizer/blob/main/docs/Evaluation.md)
+
+### 3) Results
+The evaluation results are stored in `./eval_results`, and the model prediction outputs are in `./output`.
+
+
+## Citation
+If you find our work helpful, please cite us.
+
+[AdaMLLM](https://huggingface.co/papers/2411.19930)
+```bibtex
+@article{adamllm,
+  title={On Domain-Specific Post-Training for Multimodal Large Language Models},
+  author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
+  journal={arXiv preprint arXiv:2411.19930},
+  year={2024}
+}
+```
+
+[Adapt LLM to Domains](https://huggingface.co/papers/2309.09530) (ICLR 2024)
+```bibtex
+@inproceedings{
+cheng2024adapting,
+title={Adapting Large Language Models via Reading Comprehension},
+author={Daixuan Cheng and Shaohan Huang and Furu Wei},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=y886UXPEZ0}
+}
+```