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Hello!
I´m trying to reproduce the results of your paper as a baseline for my thesis. However, I´m not able to reach the same results for pretraining on UCF101 as indicated in tables 1 & 3 (81.2% t…
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I run inference.py with pretrained model:
Traceback (most recent call last):
File "inference.py", line 78, in
main()
File "inference.py", line 32, in main
model.load_state_dict(check…
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how this data look like?('i','x','y' folder)
![2018-10-05 19-55-22](https://user-images.githubusercontent.com/39176743/46533848-b3cf5280-c8d8-11e8-8fa5-83477e438435.png)
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when I use distribution training on GPUs, the process ids are the same and the GPU utilization rate is zero.
![rate](https://user-images.githubusercontent.com/42733639/112492954-b1b92100-8dbc-11eb-9…
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Thank you for sharing your implementation. And I have a question.
The model is trained on UCF101 frames resized to 256x256, but how I test the model using original size frames. The paper said , We t…
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hi,Dear,
glad to see the project,
but a little confused,
when I run
`bash ./list/convert_video_to_images.sh .../UCF101 5`
error
```
./list/convert_video_to_images.sh: line 2: $'\r': command no…
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Hello,
I am trying to recreate the results on UCF-101 using the provided pretrained models for `PlayingViolin` and `IceDancing`. I am using the `test_refine.py` script for testing, but I am getting…
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when I use the provided docker [image](https://hub.docker.com/r/bitxiong/tsn/) to extract the optical_flow images using extract_optical_flow.sh the images are not created.
The OUT_FOLDER gets fille…