Update README.md

README.md

@@ -34,6 +34,13 @@ configs:
 # SPARK (multi-vision Sensor Perception And Reasoning benchmarK)
 
 <!-- Provide a quick summary of the dataset. -->
+<p align="center">
+  <img src="resources/problems.png" :height="300px" width="600px">
+</p>
+
+<p align="center">
+  <img src="resources/examples.png" :height="400px" width="800px">
+</p>
 
 SPARK can reduce the fundamental multi-vision sensor information gap between images and multi-vision sensors. We generated 6,248 vision-language test samples automatically to investigate multi-vision sensory perception and multi-vision sensory reasoning on physical sensor knowledge proficiency across different formats, covering different types of sensor-related questions.
 
@@ -42,11 +49,38 @@ SPARK can reduce the fundamental multi-vision sensor information gap between ima
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
-
-### Direct Use
+you can easily download the dataset as follows:
+```python
+from datasets import load_dataset
+test_dataset = load_dataset("topyun/SPARK", split="train")
+```
+
+Additionally, we have provided two example codes for evaluation: Open Model([**test.py**](https://github.com/top-yun/SPARK/blob/main/test.py)) and Closed Model([**test_closed_models.py**](https://github.com/top-yun/SPARK/blob/main/test_closed_models.py)). You can easily run them as shown below.
+
+If you have 4 GPUs and want to run the experiment with llava-1.5-7b, you can do the following:
+```bash
+accelerate launch --config_file utils/ddp_accel_fp16.yaml \
+--num_processes=4 \
+test.py \
+--batch_size 1 \
+--model llava \
+```
+
+When running the closed model, make sure to insert your API KEY into the [**config.py**](https://github.com/top-yun/SPARK/blob/main/config.py) file.
+If you have 1 GPU and want to run the experiment with gpt-4o, you can do the following:
+```bash
+accelerate launch --config_file utils/ddp_accel_fp16.yaml \
+--num_processes=$n_gpu \
+test_closed_models.py \
+--batch_size 8 \
+--model gpt \
+--multiprocess True \
+```
+
+### Tips
+The evaluation method we've implemented simply checks whether 'A', 'B', 'C', 'D', 'yes', or 'no' appears at the beginning of the sentence.
+So, if the model you're evaluating provides unexpected answers (e.g., "'B'ased on ..." or "'C'onsidering ..."), you can resolve this by adding "Do not include any additional text." at the end of the prompt.
 
-<!-- This section describes suitable use cases for the dataset. -->
 
 
 ### Source Data