naviocean / pseudo-3d-pytorch Goto Github PK

hi, @naviocean , could you provide kinetics_i3d_model_rgb.pth you used in the i3d code? Thanks very much.

Thanks for the great work :D, Just want to ask a simple question.
Why did you ignore background class in MERL dataset ??

Sorry to disturb you!
In train.py:

            if self.pretrained:
                self.model = I3D(num_classes=400, modality='rgb')
                self.model.load_state_dict(
                    torch.load('kinetics_i3d_model_rgb.pth'))

Could you tell me where I can get the I3D pretrained moel "kinetics_i3d_model_rgb.pth"?
thank you very much!

Excuse me. I would like to ask if the accuracy 81.6% you gave on UCF101 dataset is the top1 or the top5?
Since I only have one piece of 1070Ti, my batchsize can only be set to 6.
I trained from scratch according to the steps you gave, and found that the loss cannot converge.
On the test dataset, the accuracy of top1 is 42% and that of top5 is 68%
Do you think the batchsize might have influenced the final result? Thanks for your help.

Excuse me, when I have prepared UCF101, the problem is produce. Looking forward your reply. Thanks in advance.
python main.py /home/feng/pseudo-3d-pytorch/data
UCF101 data set
Loading P3D model
=> creating model P3D
Traceback (most recent call last):
File "main.py", line 52, in
main()
File "main.py", line 48, in main
Training(name_list=name_list, num_classes=num_classes, modality='RGB', **args)
File "/home/feng/pseudo-3d-pytorch/train.py", line 43, in init
self.train_loader, self.val_loader = self.loading_data()
File "/home/feng/pseudo-3d-pytorch/train.py", line 206, in loading_data
pin_memory=True)
File "/usr/local/lib/python2.7/dist-packages/torch/utils/data/dataloader.py", line 145, in init
sampler = RandomSampler(dataset)
File "/usr/local/lib/python2.7/dist-packages/torch/utils/data/sampler.py", line 61, in init
"value, but got num_samples={}".format(self.num_samples))
ValueError: num_samples should be a positive integer value, but got num_samples=0