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How do you set hyperparameters? #3
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hello, with the following command, python main.py --train --ckpt_path ckpt --gpu_ids 0 --batch_size 64 --lorb base --modulation Normal --epochs 100 --dataset CREMAD --gs_flag I also got the result of 68.8. When I set the batch size as 16 and av_alpha as 0.55, I got the result of 73.6, which is still far from the result in the paper. Are there any else parameters? |
@Cecile-hi Hi, I set batch_size=16, epoch=200, lr_decay_step=150, and other hyperparameters to default. I got a score of 0.800 on the CREMA-D dataset, which is as good as expected. Here is the figure of the accuracy curve: |
Hello, how did I obtain the JSONL file for handling food101 in the extract_token.py file? The dataset I downloaded does not have a suitable file |
@hubaak Hi! I’d like to know how you implemented the uni-modal late fusion algorithm. Did you simply add a single-layer MLP after the ResNet? And did this achieve a score of 81? |
Hi, I added logits for the audio model(ResNet and 1-layer fc) and visual model(ResNet and 1-layer fc) to get the prediction of uni-modal. To achieve a score of 81, the key is the setting of hyperparameters. I trained the uni-modal model with hyperparameters of batch_size=16, epoch=200, lr_decay_step=150 (others are defaulted) for CREMA-D and got 81. |
@hubaak thx a lot bro |
Hello, I wonder how you set hyperparameters, such as learning rate, batch size, and the number of epochs. We just obtained a 68 score on the Crame-D dataset using the command in the readme.
python main.py --train --ckpt_path ckpt --gpu_ids 0 --batch_size 64 --lorb base --modulation Normal --epochs 100 --dataset CREMAD --gs_flag
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