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-# [FILM](https://github.com/google-research/frame-interpolation): Windows Installation Instructions
-
-## Anaconda Python 3.9 (Optional)
-
-#### Install Anaconda3 Python3.9
-* Go to [https://www.anaconda.com/products/individual](https://www.anaconda.com/products/individual) and click the "Download" button.
-* Download the Windows [64-Bit](https://repo.anaconda.com/archive/Anaconda3-2021.11-Windows-x86_64.exe) or [32-bit](https://repo.anaconda.com/archive/Anaconda3-2021.11-Windows-x86.exe) Graphical Installer, depending on your system needs.
-* Run the downloaded (`.exe`) file to begin the installation.
-* (Optional) Check the "Add Anaconda3 to my PATH environment variable". You may get a 'red text' warning of its implications, you may ignore it for this setup.
-
-#### Create a new Anaconda virtual environment
-* Open a new Terminal
-* Type the following command:
-```
-conda create -n frame_interpolation pip python=3.9
-```
-* The above command will create a new virtual environment with the name `frame_interpolation`
-
-#### Activate the Anaconda virtual environment
-* Activate the newly created virtual environment by typing in your terminal (Command Prompt or PowerShell)
-```
-conda activate frame_interpolation
-```
-* Once activated, your terminal should look like:
-```
-(frame_interpolation) <present working directory> >
-```
-
-## NVIDIA GPU Support
-#### Install CUDA Toolkit
-* Go to [https://developer.nvidia.com/cuda-11.2.1-download-archive](https://developer.nvidia.com/cuda-11.2.1-download-archive) and select your `Windows`.
-* Download and install `CUDA Tookit 11.2.1`.
-* Additional CUDA installation information available [here](https://docs.nvidia.com/cuda/archive/11.2.2/cuda-installation-guide-microsoft-windows/index.html).
-
-#### Install cuDNN
-* Go to [https://developer.nvidia.com/rdp/cudnn-download](https://developer.nvidia.com/rdp/cudnn-download).
-* Create a user profile (if needed) and login.
-* Select `cuDNN v8.1.0 (January 26th, 2021), for CUDA 11.0,11.1 and 11.2`.
-* Download [cuDNN Library for Widnows (x86)](https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.1.0.77/11.2_20210127/cudnn-11.2-windows-x64-v8.1.0.77.zip). 
-* Extract the contents of the zipped folder (it contains a folder named `cuda`) into `<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\`. `<INSTALL_PATH>` points to the installation directory specified during CUDA Toolkit installation. By default, `<INSTAL_PATH> = C:\Program Files`.
-
-#### Environment Setup
-* Add the following paths to your 'Advanced System Settings' > 'Environment Variables ...' > Edit 'Path', and add:
-    * <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin
-    * <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp
-    * <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include
-    * <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64
-    * <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\cuda\bin
-
-#### Verify Installation
-* Open a **new** terminal and type `conda activate frame_interpolation`.
-* Install (temporarily) tensorflow and run a simple operation, by typing:
-```
-pip install --ignore-installed --upgrade tensorflow==2.6.0
-python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
-```
-* You should see success messages: 'Created device /job:localhost/replica:0/task:0/device:GPU:0'.
-
-## FILM Installation
-* Get Frame Interpolation source codes
-```
-git clone https://github.com/google-research/frame-interpolation
-cd frame-interpolation
-```
-* Install dependencies
-```
-pip install -r requirements.txt
-conda install -c conda-forge ffmpeg
-```
-* Download pre-traned models, detailed [here](https://github.com/google-research/frame-interpolation#pre-trained-models).
-
-## Running the Codes
-* One mid-frame interpolation. Note: `python3` may not be recognized in Windows, so simply drop `3` as below.
-```
-python -m eval.interpolator_test --frame1 photos\one.png --frame2 photos\two.png --model_path <pretrained_models>\film_net\Style\saved_model --output_frame photos\output_middle.png
-```
-
-* Large resolution mid-frame interpolation: Set `block_height` and `--block_width` to subdivide along the height and width to create patches, where the interpolator will be run iteratively, and the resulting interpolated mid-patches will be reconstructed into a final mid-frame. In the example below, will create and run on 4 patches (2*2).
-```
-python -m eval.interpolator_test --frame1 photos\one.png --frame2 photos\two.png --block_height 2 --block_wdith 2 --model_path <pretrained_models>\film_net\Style\saved_model --output_frame photos\output_middle.png
-```
-* Many in-between frames interpolation
-```
-python -m eval.interpolator_cli --pattern "photos" --model_path <pretrained_models>\film_net\Style\saved_model --times_to_interpolate 6 --output_video
-```
-
-## Acknowledgments
-
-This windows installation guide is heavily based on [tensorflow-object-detection-api-tutorial](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/install.html) .