Closed tghim closed 2 years ago
Dear authors.
First, thank you for sharing your nice work! It helps me a lot to solve my task.
Btw, your paper states that the best performance is achieved when the number of encoder layers and heads is 5 and 4, respectively, but in your code, they are set to 64 and 8, respectively. Could you explain why?
Hi, thanks for your attention to OadTR. Actually, in our code, the number of decoder layers and heads is 5 and 4, you can see: "parser.add_argument('--decoder_layers', default=5, type=int, help="Number of decoder_layers")" (line 32-33) and "parser.add_argument('--decoder_num_heads', default=4, type=int, help="decoder_num_heads")" (line 40-41) in "config.py".
Dear authors.
First, thank you for sharing your nice work! It helps me a lot to solve my task.
Btw, your paper states that the best performance is achieved when the number of encoder layers and heads is 5 and 4, respectively, but in your code, they are set to 64 and 8, respectively. Could you explain why?
"parser.add_argument('--enc_layers', default=64, type=int, help="Number of enc_layers")" refers to the features' temporal length of the input. And you can try running the entire pipeline.
Thank you for your quick reply. The number of encoder layers that was stated in your paper was 3, but I was mistaken for 5. So, you mean that "--enc_layers" indicates "T" in your paper?
Thank you for your quick reply. The number of encoder layers that was stated in your paper was 3, but I was mistaken for 5. So, you mean that "--enc_layers" indicates "T" in your paper?
Yes, that's right.
Thank you for your answer!
Dear authors.
First, thank you for sharing your nice work! It helps me a lot to solve my task.
Btw, your paper states that the best performance is achieved when the number of encoder layers and heads is 5 and 4, respectively, but in your code, they are set to 64 and 8, respectively. Could you explain why?