how to train train_siamRPN ? about siamdw HOT 6 CLOSED

how to train train_siamRPN ?
just change MODEL:"SiamRPNRes22" in yaml ?
I think it need rpn loss function .

1. Only SiamFC training code is provided now.
2. If you want to train SiamRPN, dataloader, loss function, connect head, training strategy should be modified.
3. YTB data is too large to upload. But you can try other data like GOT10K.
4. SiamRPN is much more severly influenced by hyper-parameter. Please pay attention to this.

from siamdw.

how to train train_siamRPN ?
just change MODEL:"SiamRPNRes22" in yaml ?
I think it need rpn loss function .

1. Only SiamFC training code is provided now.
2. If you want to train SiamRPN, dataloader, loss function, connect head, training strategy should be modified.
3. YTB data is too large to upload. But you can try other data like GOT10K.
4. SiamRPN is much more severly influenced by hyper-parameter. Please pay attention to this.

Does The 4 mean that you are using the post process parameter to overfit the test dataset?

from siamdw.

how to train train_siamRPN ?
just change MODEL:"SiamRPNRes22" in yaml ?
I think it need rpn loss function .

1. Only SiamFC training code is provided now.
2. If you want to train SiamRPN, dataloader, loss function, connect head, training strategy should be modified.
3. YTB data is too large to upload. But you can try other data like GOT10K.
4. SiamRPN is much more severly influenced by hyper-parameter. Please pay attention to this.

Does The 4 mean that you are using the post process parameter to overfit the test dataset?

In fact, almost all tracking algorithms are sensitive to hyper-parameters. Reasonable hyper-parameter is important to trackers.

from siamdw.

how to train train_siamRPN ?
just change MODEL:"SiamRPNRes22" in yaml ?
I think it need rpn loss function .

1. Only SiamFC training code is provided now.
2. If you want to train SiamRPN, dataloader, loss function, connect head, training strategy should be modified.
3. YTB data is too large to upload. But you can try other data like GOT10K.
4. SiamRPN is much more severly influenced by hyper-parameter. Please pay attention to this.

Does The 4 mean that you are using the post process parameter to overfit the test dataset?

In fact, almost all tracking algorithms are sensitive to hyper-parameters. Reasonable hyper-parameter is important to trackers.

and to each test video.

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pay attention

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how to train train_siamRPN ?
just change MODEL:"SiamRPNRes22" in yaml ?
I think it need rpn loss function .

SiamRPN+ training code have been uploaded. Training-Testing-Tuning is included in siamese_tracking/onekey.py. There is a tutorial for how to implement your ideas in our framework. See train.md.

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