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+# Human Action Recognition 
+```diff
++ 12/4/2019: This is work-in-progress.
+```
+
+
+This directory provides examples and best practices for building human action recognition systems. Our goal is to enable the users to bring their own datasets and train a high-accuracy model easily and quickly. To this end, we provide example notebooks with pre-set default parameters shown to work well on a variety of datasets.
+
+The R(2+1)D model is used for this scenario. This model architecture was originally presented in "A Closer Look at Spatiotemporal Convolutions for Action Recognition (2017)" paper from Facebook AI group.
+
+<img src="media/model_arch.jpg" width="300" height="300" />
+
+## Notebooks
+This project provides utility scripts to fine-tune the model and examples notebooks as follows:
+
+| Notebook | Description |
+| --- | --- |
+| [00_webcam](00_webcam.ipynb) | A real-time inference example on Webcam stream |
+| [01_training_introduction](01_training_introduction.ipynb) | An example of training R(2+1)D model on HMDB-51 dataset |
+| [02_video_transformation](02_video_transformation.ipynb) | Examples of video transformations | 
+
+Specifically, we use the model pre-trained on 65 million social media videos (IG) presented in "[Large-scale weakly-supervised pre-training for video action recognition (2019)](https://arxiv.org/abs/1905.00561)" paper.
+
+*Note: The official pretrained model weights can be found from [https://github.com/facebookresearch/vmz](https://github.com/facebookresearch/vmz) which are based on caffe2.
+In this repository, we use PyTorch-converted weights from [https://github.com/moabitcoin/ig65m-pytorch](https://github.com/moabitcoin/ig65m-pytorch).*
+
+
+## Prerequisite
+* Linux machine - We strongly recommend to use GPU machine to run the scripts and notebooks in this project smoothly (preferably [Azure NCsV3 series VMs](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/sizes-gpu#ncv3-series)).
+* To use GPUs, **CUDA toolkit v10.1** is required. Details about the CUDA installation can be found [here](https://developer.nvidia.com/cuda-downloads). Once the installation is completed, you may need to reboot the VM.
+
+
+## Installation
+1. Setup conda environment
+`conda env create -f environment.yml`
+
+1. Activate the environment
+`conda activate r2p1d`
+
+1. Install jupyter kernel
+`python -m ipykernel install --user --name r2p1d`
+
+### (Optional) Mixed-precision training
+* To use mixed-precision training via [NVIDIA-apex](https://github.com/NVIDIA/apex),
+```
+$ git clone https://github.com/NVIDIA/apex
+$ cd apex
+$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
+```
+
+
+### WebCam tunneling
+To run the model on remote GPU VM while using a local machine for WebCam streaming,
+
+1. Open a terminal window that supports `ssh` from the local machine (e.g. [git-bash for Windows](https://gitforwindows.org/)).
+
+1. Run following commandPort forward as follows (assuming Jupyter notebook will be running on the port 8888 on the VM) 
+`ssh your-vm-address -L 8888:localhost:8888` 
+
+1. Clone this repository from the VM and install the conda environment and Jupyter kernel.
+
+1. Start Jupyter notebook from the VM without starting a browser.
+`jupyter notebook --no-browser` 
+You can also set Jupyter configuration to not start browser by default.
+
+1. Copy the notebook address showing at the terminal and paste it to the browser on the local machine to open it.