silverbottlep / meta_trackers Goto Github PK

The tracking code is missing images.txt and otb_100, and the VID dataset can't be done now.

Someone tried meta_crest with pre-trained model with python 2.7, he reported it gave different result. When he changed to python 3.5, he could reproduce the result. At this point, I have no idea, hopefully have a chance to look at it later. FYI, my environment was python 3.6, pytorch 0.2.0+3f6fccd.

Hi, @silverbottlep , thaks for your excellent work. I just run your code (meta_sdnet/run_tracker.py) and find that you fix the seed in the experiment, like np.random.seed(1), torch.manual_seed(2) and torch.cuda.manual_seed(3). But i still find that the result is random, could you explain this ?

when I run "run_tracker" in meta_crest, it doesn't work and tell me that KeyError: 'conv1.weight', and i am confused,can you tell me how to solve it.

Traceback (most recent call last):
File "run_tracker.py", line 405, in
with open(list_path) as f:
FileNotFoundError: [Errno 2] No such file or directory: '../../dataset/OTB/tb_100.txt'

At line 52 in file 'meta_crest/model.py', there is a usage of variable 'weights', but I do not find where is the definition of 'weights'.

Great job!
1.the mat file is not imagenet-vgg-verydeep-16.mat but the imagenet-vgg-m.mat, you can download in
http://www.vlfeat.org/matconvnet/models/imagenet-vgg-m.mat

2. The cpu version doesn't work, so GPU is necessary for you!

Hello, thanks for your contribution 👍

• Just as the title mentioned, would you please share the results of the 30-15-pyMDNet ?

Thanks in advance!

Best regards

As far as MetaSDNet is concerned, I think meta-learning plays a role in learning good initial parameters in order to adapt the network to the changes in future frames. In addition, since the parameters learned by the meta-learning are used, only the iteration is required once in the first frame and the required samples are reduced, which brings the advantage of speed increase(MDnet iterates 30 times in the first frame, positive sample 500, negative sample 5000). In the tracking of subsequent frames, the same settings as MDNet are used. The main purpose of the article is to use meta-learning to get a good initialization parameter. I don't know if my understanding is right?
Thank you for your contribution!

Hello, I wonder that in meta_model.py, there is not self.named_parameters(), could you please tell me the reason?

Dear @silverbottlep ,
I am writing a meta-tracker on siamFC network, please give me some suggestions on the reproduce steps

Thank you so much,

Dear @silverbottlep,
Thank you for your fantastic work.
I want to change the Base CNN model of your code via another one (e.g., ResNet18). Do you have any idea that how can I do that?

Hello, I can't understand why create_graph arguments in these places(here, here) are True, because the result, grads, aren't used to calculate higher order derivative products. Am I wrong ? 😕

Thanks in advance 😄

Can you add a simple example that can work on a custom sequence/video initialization?