UCF101 Training from Scratch about c3d-tensorflow HOT 12 CLOSED

@ardasnck if you want to get the same acc with the paper you must do fine tuning from sports-1M, the paper has said it. Actually you can reference this issue #2, and I have tried that if I don't do the fine tuning I just get the 33% acc.
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Hi @ardasnck
There are 13318 videos in UCF101 dataset, I used 11318 videos for traning and 2000 videos for test, and I can get a 50% top 1 accuracy after 8000 iterations with batch_size is 64.
This is my traning from scratch top-1 accuracy curve, cross entropy curve, total loss(cross entropy + regularized loss) curve:

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Hi @ardasnck
I am a little busy in recent days, I think I can do the evaluation in next week.

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@hx173149 sure! i can't reproduce the same results with the paper on my own tensorflow implementation. So if you can get similar results after your evaluation, it would be great to add your train-from-scratch implementation in this repository.

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@hx173149 yeah i know issue #2 and also read the C3D official documentation and paper about fine-tuning. But my question is exactly on training from scratch(not fine-tuning). Actually i got 40% accuracy when I train from the scratch and you mentioned that you only reached to 33%. This https://docs.google.com/document/d/1-QqZ3JHd76JfimY4QKqOojcEaf5g3JS0lNh-FHTxLag states that they reached 45% so I was wondering what could be the potential reason for the difference? Also another observation that loss value in tensorflow is clearly higher than caffe implementation during training...

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Hi @ardasnck I think I have some free time in next days,I will reproduce my result once more... and have you ever try the caffe version code? Did it can get the 45% accuracy with training from scratch? I am curious about this problem too...
PS: I can't open the URL page you mentioned upside.
Cheers

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Hi @hx173149. I updated the link once again but I'm not sure what's happening with that...
For the training from scratch: Yes I run the caffe version of the code on my machine and I got 42.88% accuracy (note that I used batch size 16 because of my gpu capacity). I also edited my own tensorflow implementation (some minor changes) and I got 42.64%. I believe this shows that it works as it should be.
PS: In case of the link doesn't work again , I was referring to C3D-User Guide document which author provides it on his project page.

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Dear @hx173149 ,
Thank you very much for the very detailed feedback. This is great that you reach to 50% top 1 accuracy. Did you use the same train and test split that original caffe implementation used? Because paper claims that they got 45% accuracy and when I run their code on my own machine (batch size 16) i got 42.9% accuracy.

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Hello,

I also want to train from scratch but I am kind of new to Deep Learning, especially using 3d convNet. Could you briefly explain the training mechanism? Based on my understanding, you feed in 16 frames as input and a label to perform supervised learning. But do you use all the frames for training? I would really appreciate your help if you can briefly explain the whole data preparation and training process.

(I am trying to rewrite everything in Keras. So far I have defined the nets but I do not know how to prepare the video data)

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Hello @gyang1011
My training mechanism is like this:
First I will choose 64 samples randomly for each iteration
Then I will slice a 3.2 seconds(about 16 frames) randomly from each sample for training.

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@ardasnck @hx173149 @gyang1011
I trained this network and got 33% in split 1 of UCF101. However, I think the accuracy of this 8-layer convolution network should be 33%. In paper C3D, the author use a 5-layer convolution network (not 8-layer convolution), so they can get 45% in UCF101. This means that the structure of the network training from scratch and pre-trained in Sport 1M is different!

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@LongLong-Jing I think you are right, maybe there have some duplication samples among my train list and test list, I am not very sure.

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