Awesome

Linear Separability Evaluation

This repo provides the scripts to test a learned AlexNet's feature representation performance at the five different convolutional levels -- in parallel. The training lasts 36 epochs and should be finished in <1.5days.

Usage

$python eval_linear_probes.py

usage: eval_linear_probes.py [-h] [--data DATA] [--ckpt-dir DIR] [--device d]
                             [--modelpath MODELPATH] [--workers N]
                             [--epochs N] [--batch-size N]
                             [--learning-rate FLOAT] [--tencrops] [--evaluate]
                             [--img-size IMG_SIZE] [--crop-size CROP_SIZE]
                             [--imagenet-path IMAGENET_PATH]

AlexNet standard linear separability tests

optional arguments:
  -h, --help            show this help message and exit
  --data DATA           Dataset Imagenet or Places (default: Imagenet)
  --ckpt-dir DIR        path to checkpoints (default: ./test)
  --device d            GPU device
  --modelpath MODELPATH
                        path to model
  --workers N           number of data loading workers (default: 6)
  --epochs N            number of epochs (default: 36)
  --batch-size N        batch size (default: 192)
  --learning-rate FLOAT
                        initial learning rate (default: 0.01)
  --tencrops            flag to not use tencrops (default: on)
  --evaluate            flag to evaluate only (default: off)
  --img-size IMG_SIZE   imagesize (default: 256)
  --crop-size CROP_SIZE
                        cropsize for CNN (default: 224)
  --imagenet-path IMAGENET_PATH
                        path to imagenet folder, where train and val are
                        located

Settings

The settings follow the caffe code provided in Zhang et al., with optional tencrops enabled. Average pooling can be used, but max-pooling is faster and overall more common so it is used here.

Reference

If you use this code, please consider citing the following paper:

Yuki M. Asano, Christian Rupprecht and Andrea Vedaldi. "A critical analysis of self-supervision, or what we can learn from a single image." Proc. ICLR (2020)

@inproceedings{asano2020a,
  title={A critical analysis of self-supervision, or what we can learn from a single image},
  author={Asano, Yuki M. and Rupprecht, Christian and Vedaldi, Andrea},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2020},
}