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FOCL

Framework for unsupervised semantic segmentation using a novel clustering algorithm named FOCL.

Segmentation results

Installation

Clone the repository

git clone https://github.com/bercowsky/Collapsing.git

Compile Cython code

cd src/cython
python3 setup.py build_ext --inplace

Usage

Run experiments

To run experiments using STEGO features:

cd src
python3 eval_stego.py

To run experiments using DINO, DINOv2 or SAM as a feature extractor:

cd src
python3 eval.py

These scripts will extract features from a user-defined number of images, then creates meta-points for them, and finally run FOCL to assign pseudo-labels for each pixel. The script will then evaluate the performance of FOCL using the pseudo-labels and the ground truth labels, logging the results into W&B.

Each script has a number of options to select, including the number of images to use, the distance metric to use (between euclidean and 1-cosine_similarity), the radius of the meta-points and the k for the k-nearest neighbors algorithm used in FOCL. Additionally, in eval.py the user can select the model to use as a feature extractor, and the option to apply PCA to those features.

Folder structure

These scripts expect the following directory structure:

.
├── PREFIX/
│   ├── ADEChallengeData2016/
│   │   ├── annotations/
│   │   │   ├── training/
│   │   │   │   ├── *.png
│   │   │   ├── validation/
│   │   │   │   ├── *.png
│   │   ├── images/
│   │   │   ├── training/
│   │   │   │   ├── *.jpg
│   │   │   ├── validation/
│   │   │   │   ├── *.jpg
│   ├── meta_points/
│   ├── saved_models/

The PREFIX directory is the root directory of the dataset. The ADEChallengeData2016 directory contains the ADE20K dataset. The annotations directory contains the ground truth labels for the training and validation sets. The images directory contains the images for the training and validation sets. The meta_points directory is an empty directory that will contain .png files where each pixel value represent the meta_point id representing that pixel. The saved_models directory contains the saved models for the feature extractors.