Update README.md

README.md

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 MultiOOD is the first-of-its-kind benchmark for Multimodal OOD Detection, characterized by diverse dataset sizes and varying modality combinations.
 
+## Code
+https://github.com/donghao51/MultiOOD
+
 ## MultiOOD Benchmark
 MultiOOD is based on five public action recognition datasets (HMDB51, UCF101, EPIC-Kitchens, HAC, and Kinetics-600). 
 
@@ -188,8 +191,6 @@ The splits for Multimodal Near-OOD and Far-OOD Benchmarks are provided in https:
 
 An overview of the proposed framework for Multimodal OOD Detection. We introduce A2D algorithm to encourage enlarging the prediction discrepancy across modalities. Additionally, we propose a novel outlier synthesis algorithm, NP-Mix, designed to explore broader feature spaces, which complements A2D to strengthen the OOD detection performance.
 
-## Code
-https://github.com/donghao51/MultiOOD
 
 ## Contact
 If you have any questions, please send an email to donghaospurs@gmail.com