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Code for Which Tasks to Train Together in Multi-Task Learning

Trevor Standley, Amir R. Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, Silvio Savarese

ICML 2020

http://taskgrouping.stanford.edu/

  1. Install pytorch,torchvision
  2. Install apex
conda install -c conda-forge nvidia-apex
  1. (optional) install data loading speedups:
conda install -c thomasbrandon -c defaults -c conda-forge pillow-accel-avx2
conda install -c conda-forge libjpeg-turbo
  1. Get training data https://github.com/StanfordVL/taskonomy/tree/master/data The data must be aranged in
inputs:   
  root/rgb/building/point_x_view_x.png
labels:
  root/$task$/$building$/point_x_view_x.png

order.

usage example

python3 train_taskonomy.py -d=/taskonomy_data/ -a=xception_taskonomy_new -j 4 -b 96 -lr=.1 --fp16 -sbn --tasks=sdnerac -r

Pretrained models from setting 2:

https://drive.google.com/drive/folders/1XQVpv6Yyz5CRGNxetO0LTXuTvMS_w5R5?usp=sharing

to test these models on the test set:

python3 train_taskonomy.py -d=/taskonomy_data/ -a=xception_taskonomy_new -j 4 -b 256 -lr=.1 --fp16 -sbn --tasks=[task letters] --resume=setting2_models/xception_taskonomy_new_[task letters].pth.tar -t -r

(contact for models from other settings)