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README.md

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 # VideoMAE (base-sized model, fine-tuned on Something-Something-v2) 
 
-VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Something-Something-v2. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et al. and first released in [this repository](https://github.com/MCG-NJU/VideoMAE). 
+VideoMAE model pre-trained for 2400 epochs in a self-supervised way and fine-tuned in a supervised way on Something-Something-v2. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et al. and first released in [this repository](https://github.com/MCG-NJU/VideoMAE). 
 
 Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team.
 
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 ## Evaluation results
 
-This model obtains a top-1 accuracy of 80.9 and a top-5 accuracy of 94.7 on the test set of Kinetics-400.
+This model obtains a top-1 accuracy of 70.6 and a top-5 accuracy of 92.6 on the test set of Something-Something-v2.
 
 ### BibTeX entry and citation info