Comments (6)
Fucking answer!
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Same question
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We want to know more clearly!!!
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Ex your training API
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Hi, you can train the model in the usual way. Try looking at some image classification training examples. The steps are as follows: 1. Get training data (videos + ground truth), 2. Load pre-trained I3D model weights, 3. optimize them using SGD to fit to your data. The only difference to image classification example code available on the web is that you'll be loading video data, typically sets of 64-frame clips. You may want to create some preprocessing code if you want to do data augmentation. Joao
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On Thu, Sep 20, 2018 at 8:32 AM Mykola Baranov @.***> wrote: Same question — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#22 (comment)>, or mute the thread https://github.com/notifications/unsubscribe-auth/AO6qaj-m8fdygqNzu6ar1Cqgz75qRj8nks5uc0SlgaJpZM4Tf0Xl .
Hi, could you please explain more in detail? Thanks.
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Related Issues (20)
- Optical flow rescaling HOT 5
- How to create a custom action recognition model HOT 2
- Inflating pre-trained models HOT 1
- Trying to create frozen graph, so i can convert it into tflite for android
- Training with different architectures
- customize actions class in the model HOT 1
- offline usage
- rgb.npy and flow.npy HOT 3
- training from scratch HOT 2
- which one is the checkpoint? HOT 5
- The problem of receptive field in I3D paper
- missing videos
- Does the video need to be cropped? HOT 11
- Calculation of TV L1 flow HOT 1
- dependencies issues HOT 1
- Struggling to learn using Opt. Flow HOT 2
- Incompatibility issues
- Run time of I3D on edge decives
- Is there Model File(.pth or .pt) that pretrained with Imagenet+Kinetics?
- I found the pth file which pretrained on Kinetics400 HOT 1
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