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arid_ug2_2.1's Introduction

Base (Reference) Framework for UG2+ Track 2.1 Challenge: Fully Supervised Action Recognition in the Dark

This repository contains the framework for UG2+ Track 2.1 Challenge: Fully Supervised Action Recognition in the Dark.

Prerequisites

This code is based on PyTorch, you may need to install the following packages:

PyTorch >= 1.2 (tested on 1.2/1.4/1.5/1.6)
opencv-python (pip install)

Training

Training:

python train_arid_t1.py --network <Network Name>
  • There are a number of parameters that can be further tuned. We recommend a batch size of 8 per GPU. Here we provide an example where the 3D-ResNet (18 layers) network is used. This network is directly imported from torchvision. You may use any other networks by putting the network into the /network folder. Do note that it is recommended you run the network once within the /network folder to debug before you run training.

Testing

To generate the zipfile to be submitted, use the following commands:

cd predict
python predict_video.py

You may change the resulting zipfile name by changing the "--zip-file" configuration in the code, or simply by changing the configuration dynamically by

python predict_video.py --zip-file <YOUR PREFERRED ZIPFILE NAME>

Other Information

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arid_ug2_2.1's Issues

In the test mode, why are the network outputs the same?

Hello!Sorry to bother you!In the predict mode, why are the network outputs the same?

The first picture is the data input into the network. You can see that it is different. But the output of the network is the same, as shown in the second picture. What's going on?

3bbc2e7546821d5f2bf3981bb5bcedb

How to run the code from Beginners point of view?

Hi
I am a beginner in the ML field. So far I have just created my own models in Jupyter notebooks and ran them. But using a baseline model is kind of new for me. I don't know where to start from.
I understand I need to compile the models first. But can someone lay down some beginner steps from where to start with using this code?

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