ProHOC

This is the official repository for the CVPR 2025 paper: ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks.

If you have questions or would like to discuss the paper or code, don't hesitate to get in touch!

Requirements

Python dependencies are listed in requirements.txt.

Install them using:

pip install -r requirements.txt

Dataset Setup

Manually download the following datasets:

• FGVC-Aircraft
• iNaturalist 2019
• ImageNet 2012

Extract the datasets into directories of your choice.

Set Environment Variables

Before running the installation script, set the following environment variables:

# Path to the ProHOC repository
PROHOC=/path/to/prohoc/

# Paths to raw datasets
FGVCSOURCE=/path/to/fgvc-aircraft-2013b/
IMAGENETSOURCE=/path/to/imagenet/
INATSOURCE=/path/to/train_val2019/

# Path to iNat train-val splits (provided in this repo)
INATSPLITS=$PROHOC/splits_inat19

# ImageNet validation labels
IMAGENETVALLABELS=/path/to/imagenet/LOC_val_solution.csv

# Output paths
PROHOCDATA=/path/to/prohoc-data/       # Where processed data/symlinks will go
TRAINDIR=/path/to/prohoc-experiments/  # Where results/checkpoints will be saved

Install Datasets

Run the installation script:

python3 $PROHOC/install_datasets.py \
  --fgvc_source $FGVCSOURCE \
  --imagenet_source $IMAGENETSOURCE \
  --inat_source $INATSOURCE \
  --inat_splits $INATSPLITS \
  --imagenet_vallabels $IMAGENETVALLABELS \
  --destdir $PROHOCDATA

To skip installing a dataset, simply omit its source argument.

Training

Train the multi-depth networks for each dataset and hierarchy depth.

iNaturalist19

HEIGHTS=(0 1 2 3 4 5)
DSET=inat19

for HEIGHT in "${HEIGHTS[@]}"; do
  python3 $PROHOC/main_multidepth.py \
    --datadir $PROHOCDATA/$DSET/ \
    --hierarchy $PROHOC/hierarchies/$DSET.pth \
    --traindir $TRAINDIR/$DSET/H$HEIGHT \
    --id_split $PROHOC/data/$DSET-id-labels.csv \
    --height $HEIGHT \
    --epochs 90 \
    --lr 0.01
done

FGVC-Aircraft

HEIGHTS=(0 1 2)
DSET=fgvc-aircraft

for HEIGHT in "${HEIGHTS[@]}"; do
  python3 $PROHOC/main_multidepth.py \
    --datadir $PROHOCDATA/$DSET/ \
    --hierarchy $PROHOC/hierarchies/$DSET.pth \
    --traindir $TRAINDIR/$DSET/H$HEIGHT \
    --id_split $PROHOC/data/$DSET-id-labels.csv \
    --height $HEIGHT \
    --epochs 90 \
    --lr 0.01
done

SimpleHierImagenet

HEIGHTS=(0 1 2 3 4 5 6 7 8 9 10)
DSET=simple-hier-imagenet
DATASOURCE=imagenet

for HEIGHT in "${HEIGHTS[@]}"; do
  python3 $PROHOC/main_multidepth.py \
    --datadir $PROHOCDATA/$DATASOURCE/ \
    --hierarchy $PROHOC/hierarchies/$DSET.json \
    --traindir $TRAINDIR/$DSET/H$HEIGHT \
    --id_split $PROHOC/data/$DSET-id-labels.csv \
    --height $HEIGHT \
    --epochs 150 \
    --lr 0.05
done

Note: Although the hierarchy is named simple-hier-imagenet, it is built using a class subset from the ImageNet dataset. Therefore, the data directory (--datadir) points to imagenet, not simple-hier-imagenet.

Training jobs can also be run in parallel if your system supports it.

Generate Predictions

Generate validation logits for ID and OOD data to enable evaluating the hierarchical inference.

FGVC-Aircraft

DSET=fgvc-aircraft
HEIGHTS=(0 1 2)

for HEIGHT in "${HEIGHTS[@]}"; do
  python3 $PROHOC/gather_vallogits_multidepth.py \
    --datadir $PROHOC/$DSET/ \
    --traindir $TRAINDIR/$DSET/H$HEIGHT \
    --height $HEIGHT \
    --id_split $PROHOC/data/$DSET-id-labels.csv \
    --hierarchy $PROHOC/hierarchies/$DSET.json
done

iNaturalist19

DSET=inat19
HEIGHTS=(0 1 2 3 4 5)

for HEIGHT in "${HEIGHTS[@]}"; do
  python3 $PROHOC/gather_vallogits_multidepth.py \
    --datadir $PROHOC/$DSET/ \
    --traindir $TRAINDIR/$DSET/H$HEIGHT \
    --height $HEIGHT \
    --id_split $PROHOC/data/$DSET-id-labels.csv \
    --hierarchy $PROHOC/hierarchies/$DSET.json
done

SimpleHierImagenet

DSET=simple-hier-imagenet
HEIGHTS=(0 1 2 3 4 5 6 7 8 9 10)

for HEIGHT in "${HEIGHTS[@]}"; do
  python3 $PROHOC/gather_vallogits_multidepth.py \
    --datadir $PROHOC/$DSET/ \
    --traindir $TRAINDIR/$DSET/H$HEIGHT \
    --height $HEIGHT \
    --id_split $PROHOC/data/$DSET-id-labels.csv \
    --hierarchy $PROHOC/hierarchies/$DSET.json
done

---

Evaluate ProHOC

Evaluate hierarchical predictions:

DSET=inat19

python3 $PROHOC/gather_hinference.py \
  --basedir $TRAINDIR/$DSET/ \
  --uncertainty_methods compprob entcompprob \
  --id_split $PROHOC/data/$DSET-id-labels.csv \
  --hierarchy $PROHOC/hierarchies/$DSET.json

Results will be saved to:

$TRAINDIR/$DSET/hinference-inat19.result

You can load and inspect the results using:

import torch
torch.load(...)

Hierarchies

The class hierarchies are defined in JSON files located in the hierarchies directory. Each JSON file represents a tree-structured class hierarchy, where every node corresponds to a class in the dataset.

Each node in the JSON files contains the following fields:

• name: A string identifying the class. This should match the corresponding class directory name of the dataset.
• description: A human-readable description of the class, useful for clarifying non-descriptive class names (such as WordNet IDs).
• children: A list of child nodes, each following the same structure, representing subclasses. This list is empty for leaf nodes.

To define a new hierarchy, create a JSON file with the same structure. The top-level node needs to be named "root".

Notes

The ID/OOD splits of the datasets are specified in the csv files contained in $PROHOC/data/. Other ID/OOD splits can be used by creating new files with other classes.

Out-of-hierarchy datasets can be evaluated by first generating their predictions with $PROHOC/gather_faroodlogits_multidepth and then including them when running $PROHOC/gather_hinference.py using the --farood *MY-DATASET* flag with --betas 1.

Acknowledgement

Parts of this repository are adapted from rwl93/hierarchical-ood. Thanks to the authors for their valuable contributions.