Trained weights about cage_expression_inference

[FelixMaetzler]

Hi @JSOlier,
i committed all the scripts we used for our final paper. You can find them in the models directory. In every subdirectory there is a train.py and a generate_csv.py file. The first one generates the model.pt (it's too large to commit to github) and the second uses this model.pt and creates the inference.csv.
The evaluation.py then uses (or creates) the inference.csv and prints the results.

As described in the paper, the VA models where trained on top of the combined models.

I hope this helps you.
Please feel free to contact us again if you have further questions.

Best regards,
Felix

[ZayneHuang]

Hi @FelixMaetzler,

Thanks for your contribution. Could you share the checkpoint files on other platforms like Google Drive?

Or, if I want to train the model on AffectNet, I wonder which model achieved the best accuracy? According to the paper, MaxViT valence−arousal won the best performance in relation to valence and arousal prediction on AffectNet 8, which is this model in the respository. Am I right?

[ZayneHuang]

Hi @ZayneHuang,

your right, AffectNet8_Maxvit_VA is our overall best model regarding valence and arousal estimation. Please keep in mind that we initialized the model weights of AffectNet8_Maxvit_Combined in this Script. Therefore, you have to train this model too.

Best regards, Niklas

Thanks for your reply and advice : )

[wagner-niklas]

Hi @JSOlier
thanks for the interest in our project. We will update our files this afternoon.
Best regards,
Niklas

[wagner-niklas]

Hi @ZayneHuang,

your right, AffectNet8_Maxvit_VA is our overall best model regarding valence and arousal estimation. Please keep in mind that we initialized the model weights of AffectNet8_Maxvit_Combined in this Script. Therefore, you have to train this model too.

Best regards,
Niklas

[Varun-GP]

Thanks for your contribution. Could you please share the checkpoint files maybe on a Google Drive Link? It would greatly help in understanding the model prediction from live videos.