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Open-Set Likelihood Maximization for Few-Shot Learning

Python Versions Code style: black

Code for the paper Open-Set Likelihood Maximization for Few-Shot Learning , accepted at CVPR 2023.

Getting started

Installation

virtualenv venv --python=python3.8 
source venv/bin/activate
pip install -r requirements.txt

On top of common packages, this project uses pyod, timm and easyfsl.

Download models

This code uses several models, but most will be automatically downloaded. The only models you will need to download manually are:

  1. mini-ImageNet FEAT ResNet-12 and tiered-ImageNet FEAT ResNet-12. Place them under data/models/feat/resnet12_{dataset}_feat.pth, with dataset in [mini_imagenet, tiered_imagenet].

  2. mini-ImageNet pretrained ResNet-12 & WRN 28-10 and tiered-ImageNet pretrained ResNet-12 & WRN 28-10. Place them under data/models/standard/{arch}_{dataset}_feat.pth, with arch in [resnet12, wrn2810] and dataset in [mini_imagenet, tiered_imagenet].

Download data

Mini-ImageNet

Download ILSVRC2015 and place all class-wise directories images in data/mini_imagenet/images (e.g. data/mini_imagenet/images/nXXXXXXX/nXXXXXXX_XXXXX.JPEG). You may also download ImageNet from academic torrents here.

Tiered-ImageNet

Download from here and place under data/tiered_imagenet.

ImageNet

Place ImageNet in data/ilsvrc_2012 so that you have:

data/ilsvrc_2012
├── train
│   ├── n01440764
│   │   ├── n01440764_10026.JPEG
│   │   ├── ...
│   ├── ...
├── val
│   ├── ILSVRC2012_val_00000001.JPEG
│   ├── ...
├── test
│   ├── ILSVRC2012_test_00000001.JPEG
│   ├── ...

Aircraft

Execute make aircraft

Fungi

Execute make fungi

CUB

Download from https://data.caltech.edu/tindfiles/serve/1239ea37-e132-42ee-8c09-c383bb54e7ff/

Extract features

Because most methods work directly on top of extracted features, we factorize this part and extract features from each model once and for all, before runnning methods. In order to extract features for all splits, all datasets and all architectures, please run:

make extract_all

This operation make take quite some time.

Reproducing results

All commands to reproduce any results in the paper is contained as a recipe in the Makefile. We detail in what follows every command:

Hyperparameter tuning (optional)

By default, the best hyperparameters found are already written down in the config files. If you wish to re-do the tuning pipeline from scratch, please following the following guidelines. First, run make tuning to run hyperparameter tuning on all methods on mini-ImageNet. To know which hyperparameters are being searched, refer to configs/detectors.yaml. Here is an example:

ABOD:
  default: {<shot>: {'n_neighbors': 5, 'method': 'fast'},
              }
  tuning: 
    hparams2tune: ['n_neighbors', 'method'] # name of the argument to tune
    hparam_values: {<shot>: [[3, 5], ['fast', 'default']], # for each argument, define the list of values over which to iterate. 
                    }

In the previous example, the tuning pipeline will perform a grid search over hyperparameters 'n_neighbors' and 'method', over the values [3, 5] and [fast, default] respectively. Note that <shot> key allows you to potentially define different sets of hyper-parameters according to the number of shots available. If you only write arguments for shot=1, it will just use these arguments, regardless of the shot.

Once the tuning is over, results will be logged to results/tuning/*. You can directly log the best configs found for each methods (computed as the best trade-off between accuracy and ROCAUC) by executing log_best_configs.

Benchmarks (Table 2 and 4)

To reproduce the standard benchmarking Table (2 and 4) in the paper, please execute make benchmark. Results will be saved to results/benchmarks. You can also directly log the results in a Markdown format by running make log_benchmark.

Cross-domain experiments (spider charts)

Run make spider_charts. Once this is done, run make plot_spider_charts, and go to plots to see the plots.

Model-agnostic (horizontal barplots)

Run make model_agnosticity. Once this is done, run make plot_model_agnosticity, and go to plots to see the plots.

Additional features

Deploying code/data/models

The code support easy deployement to a remote server through rsync. If this can be helpful for you, please change the SERVER_IP variable and SERVER_PATH variables that respectively should contain the ip address of the remote server, and the path where the repo should be deployed. We let the reader refer to the Deployment / Imports part of the Makefile for all useful recipes.

Archiving / Restoring results

Once a set of results have been performed, you can archive them by running make archive and follow the subsequent instructions to reduce chances of losing them. This will store them in the archive/ folder. To restore them back to results/ at any time, run make restore and follow the instructions.

Acknowledgements

We would like to thank authors from FEAT for open-sourcing their code and publicly releasing checkpoints, and contributors to timm library for their excellent work in keeping up with most recent architectures.