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Contrast sensitivity function in deep networks

The contrast sensitivity function (CSF) is a fundamental signature of the visual system that has been measured extensively in several species. It is defined by the visibility threshold for sinusoidal gratings at all spatial frequencies. Here, we investigated the CSF in deep neural networks using the same 2AFC contrast detection paradigm as in human psychophysics. Read more at: bioRxiv or Neural Networks

A more generic version of the linear probing method used in this repository and manuscript is available in a pip package called osculari PyPi Status. Use the osculari package GitHub Latest Release) for any other psychophysical experiments you wish to conduct with pretrained deep networks.

Usage

Train the linear classifier contrast discriminator

To train a linear classifier on top of a frozen pretrained network, run the command below:

python ../src/train.py \ 
-aname $MODEL --transfer_weights $MODEL_PATH $LAYER "classification" \
-dname "bw" --data_dir $DATA_DIR -b $BATCH_SIZE \
--experiment_name $EXPERIMENT_NAME -j $J --gpu 0 --output_dir $OUT_DIR \
--target_size 224 --epochs 10 --colour_space "imagenet_rgb" \
--vision_type "trichromat" --train_samples 15000 --val_sample 100 \
--contrast_space "rgb" --classifier "nn"

Arguments:

  • $MODEL is the pretrained network, e.g. resnet50.
  • $MODEL_PATH is the path to the weights of pretrained network, to obtain default weights pass the same name as $MODEL, e.g. resnet50.
  • $LAYER is the layer to cut off the network, e.g. fc.
  • $DATA_DIR the directory to the binary shape dataset, download from here.

Measuring the network/layer CSF

After training the linear classifier, you can measure its CSF with the following command:

python ../src/test.py -aname $MODEL_PATH --contrast_space $CONTRAST_SPACE \
--experiment_name $CONTRAST_SPACE --target_size 224 --output_dir $OUT_DIR \
--colour_space "imagenet_rgb"  --vision_type "trichromat" \
--print_freq 1000 --gpu 0 --classifier "nn" --mask_image "fixed_cycle"

Arguments:

  • $MODEL_PATH the path to saved checkpoint from the training procedure.
  • $CONTRAST_SPACE can be one of these three strings "lum_yog", "rg_yog", "yb_yog" corresponding to luminance, red-green and yellow-blue channels.

Citation

Akbarinia, A., Morgenstern, Y. and Gegenfurtner, K.R., 2023. Contrast sensitivity function in deep networks. Neural Networks, 164, pp.228-244.