GazeCapture | Eye Tracking for Everyone | Machine Learning library
kandi X-RAY | GazeCapture Summary
kandi X-RAY | GazeCapture Summary
This is the README file for the official code, dataset and model release associated with the 2016 CVPR paper, "Eye Tracking for Everyone".
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Top functions reviewed by kandi - BETA
- Validate the model
- Update statistics
- Train the model
- Read json from file
- Log an error message
- Crop an image
- Prepare a path
- Save checkpoint
- Load checkpoint from file
- Adjust learning rate for given epoch
GazeCapture Key Features
GazeCapture Examples and Code Snippets
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QUESTION
I'm attempting to code in Tensorflow the gazecapture model given here:
http://gazecapture.csail.mit.edu/cvpr2016_gazecapture.pdf
Near the end of the model, the eye data, face data, and face grid data have been reduced to three different fully connected layers, of size 1x128, 1x64, and 1x128 respectively. The weights do not appear to be shared but are combined somehow into one final fully connected layer. I am missing the link that joins these together to form the final fully connected layer.
How does this go about? Are there examples of this elsewhere?
The github of the original project is located here if that helps:
https://github.com/CSAILVision/GazeCapture
Thank you! I apologize in advance if this is an elementary mistake.
...ANSWER
Answered 2017-May-11 at 14:40Usually, they concat outputs from multiple fully connected layers and feed into next one. Following is the code snippet that does concatenation. You can find it in itracker_train_val.prototxt
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Install GazeCapture
You can use GazeCapture like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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