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Pre-reqs

CUB Data

  1. Download CUB-200-2011 images somewhere:
wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz && tar -xf CUB_200_2011.tgz
  1. Download our CUB annotation mat files and pre-computed SfM outputs. Do this from the cmr/ directory, and this should make cmr/cachedir directory:

wget https://people.eecs.berkeley.edu/~shubhtuls/cachedir/cmr/cachedir.tar.gz

Please you use this temporary link for the moment.

tar -vzxf cachedir.tar.gz

Computing SfM

We provide the computed SfM. If you want to compute them yourself, run via matlab:

cd preprocess/cub
main

You can find the pre-computed SfM for PASCAL 3D+ cars and aeroplanes here.

Model training

Change the name to whatever you want to call. Also see shape.py to adjust hyper-parameters (for eg. increase tex_loss_wt and text_dt_loss_wt if you want better texture, increase texture resolution with tex_size). See nnutils/mesh_net.py and nnutils/train_utils.py for more model/training options.

cmd='python -m cmr.experiments.shape --name=bird_net --display_port 8087'

Evaluation

We provide evaluation code to compute the IOU curves in the paper. Command below runs the model with different ablation settings. Run it from one directory above the cmr directory.

python -m cmr.benchmark.run_evals --split val  --name bird_net --num_train_epoch 500

Then, run

python -m cmr.benchmark.plot_curvess --split val  --name bird_net --num_train_epoch 500

in order to see the IOU curve.