Replication of RITNet

About the project

• The source paper and github project of RITNet is here:
  • Source: https://arxiv.org/abs/1910.00694
  • Git: https://github.com/AayushKrChaudhary/RITnet

@inproceedings{chaudhary2019ritnet,
  title={RITnet: real-time semantic segmentation of the eye for gaze tracking},
  author={Chaudhary, Aayush K and Kothari, Rakshit and Acharya, Manoj and Dangi, Shusil and Nair, Nitinraj and Bailey, Reynold and Kanan, Christopher and Diaz, Gabriel and Pelz, Jeff B},
  booktitle={2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
  pages={3698--3702},
  year={2019},
  organization={IEEE}
}

Requirements:

• Anaconda
• Conda environment with Python 3.8

Results:

• This is the result trained from 1 epoch of 8000 objects (~34,000 objects in the training set). These are trained with 1 GPU NVIDIA GTX 1060 Max-Q 6 Gb. Trained with normal CE loss.

  Input:

  

  Inference

  

• This is the result trained from 10 epoch of 34,000 objects per epoch. These are trained with 1 GPU NVIDIA RTX 3080 12 Gb. Trained with normal CE loss

  History

  

  Input

  

  Inference

  

• This is the result trained from 10 epoch of 34,000 objects per epoch. These are trained with 1 GPU NVIDIA RTX 3080 12 Gb. Trained with normal CE and GDL loss.

  History

  

  Input

  

  Inference

  

• This is the result trained from 10 epoch of 34,000 objects per epoch. These are trained with 1 GPU NVIDIA RTX 3080 12 Gb. Trained with normal CE, GDL loss, and Surface Loss.

  History

  

  Input

  

  Inference

  

• Surface Loss: This is the example result of generating distance matrix based on preprocessed label.

  Input (not preprocessed label)

  

  Distance matrix

  

• BAL Loss:

  • This is the example result of canny edge detection based on preprocessed label.

  Input (not preprocessed label)

  

  Canny

  
  • Model with BAL Loss integration is still in debugging stage and has not been trained.

Misc

• Install TensorRT

python -m pip install C:\Users\GBURG-4\Documents\TensorRT-7.2.2.3.Windows10.x86_64.cuda-11.1.cudnn8.0\TensorRT-7.2.2.3\graphsurgeon\graphsurgeon-0.4.5-py2.py3-none-any.whl

python -m pip install C:\Users\GBURG-4\Documents\TensorRT-7.2.2.3.Windows10.x86_64.cuda-11.1.cudnn8.0\TensorRT-7.2.2.3\uff\uff-0.6.9-py2.py3-none-any.whl

python -m pip install C:\Users\GBURG-4\Documents\TensorRT-7.2.2.3.Windows10.x86_64.cuda-11.1.cudnn8.0\TensorRT-7.2.2.3\onnx_graphsurgeon\onnx_graphsurgeon-0.2.6-py2.py3-none-any.whl