https://github.com/yukimasano/linear-probes

Evaluating AlexNet features at various depths
https://github.com/yukimasano/linear-probes

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Evaluating AlexNet features at various depths

• Host: GitHub
• URL: https://github.com/yukimasano/linear-probes
• Owner: yukimasano
• Created: 2020-02-14T14:30:58.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-10-13T11:53:28.000Z (over 3 years ago)
• Last Synced: 2024-01-16T15:40:53.194Z (5 months ago)
• Language: Python
• Size: 16.6 KB
• Stars: 36
• Watchers: 3
• Forks: 9
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Lists

• awesome-self-supervised-learning - [code
• awesome-stars - yukimasano/linear-probes - Evaluating AlexNet features at various depths (Python)
• awesome-medical-ssl - [code

README

        
# 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.](https://github.com/richzhang/colorization), 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},

}

```