If you want to train your own B-cosified models using this repository or are interested in reproducing the results, you can set up the development environment as follows:

Using conda:

conda env create -f environment.yml
conda activate bcosification

Using pip:

conda create --name bcosification python=3.12
pip install -r requirements.txt

You can either set the paths in bcos/settings.py or set the environment variables

1. DATA_ROOT
2. IMAGENET_PATH
3. CC3M_PATH
4. IMAGENET_RN50_ZEROSHOT_WEIGHTS_PATH (for zeroshot evaluation of CLIP models)

to the paths of the data directories.

• For ImageNet, the IMAGENET_PATH environment variable should point to the directory containing the train and val directories.

• For CC3M, the CC3M_PATH environment variable should point to the directory containing the training and validation directories with *.tar, *_stats.json, and *.parquet files. For more instructions, please check here.

• For zeroshot evaluation of CLIP models during training, the IMAGENET_RN50_ZEROSHOT_WEIGHTS_PATH environment variable should point to the weights provided in the release.

For evaluating or training the models, you can use the evaluate.py and train.py scripts, as follows:

For single-GPU training:

python train.py \
    --dataset ImageNet \
    --base_network bcosification \
    --experiment_name resnet_18 

For distributed training:

python run_with_submitit.py \
    --dataset ImageNet \
    --base_network vit_bcosification \
    --experiment_name bcosifyv2_bcos_simple_vit_ti_patch16_224_0.001_gapReorder-seed=5 \
    --distributed \
    --gpus 4 \
    --node 1 \
    --timeout 8 \
    --wandb_logger \
    --wandb_project bcosification \
    --explanation_logging

You can use evaluate the accuracy of the models on the ImageNet validation set using:

python evaluate.py \
    --dataset ImageNet \
    --base_network bcosification \
    --experiment_name resnet_18 \
    --reload last

• base_network: bcosification for CNNs, or vit_bcosification for ViTs.
• experiment_name: check the List of experiments section below.
• To evaluate the pre-trained B-cosified ImageNet models, please follow the instructions given below in the Checkpoints section.

For evaluating B-cosified CLIP models, use CLIP Benchmark as follows:

Zeroshot:

 python CLIP_benchmark/clip_benchmark/cli.py eval \
    --dataset=wds/imagenet1k \
    --model_type=bcos_clip \
    --output=benchmark_{dataset}_{model}_{task}.json \
    --dataset_root=https://huggingface.co/datasets/clip-benchmark/wds_{dataset_cleaned}/tree/main \
    --model=resnet_50_clip_b2_noBias_randomResizedCrop_sigLip_ImageNet_bcosification \
    --pretrained=experiments/ImageNet/clip_bcosification

Linear Probe:

python CLIP_benchmark/clip_benchmark/cli.py eval \
    --task=linear_probe \
    --dataset=wds/imagenet1k \
    --model_type=bcos_clip \
    --output=benchmark_{dataset}_{model}_{task}.json \
    --dataset_root=https://huggingface.co/datasets/clip-benchmark/wds_{dataset_cleaned}/tree/main \
    --model=resnet_50_clip_b2_noBias_randomResizedCrop_sigLip_ImageNet_bcosification \
    --pretrained=experiments/ImageNet/clip_bcosification/ \
    --batch_size=512 \
    --fewshot_lr 0.1 \
    --fewshot_epochs 20 \
    --train_split train \
    --test_split test

• --dataset: use wds/{dataset} for available Zeroshot and Linear Probe datasets.
• --model_type: bcos_clip, bcos_clip_cc3m, text2concept_clip, standard_clip.
• --model: for model types bcos_clip and bcos_clip_cc3m, check the List of experiments section for model names; for standard_clip use RN50; for text2concept_clip not required.

Note: For CLIP models, automatic zeroshot evaluation is done at the start of every epoch.

For localisation analysis of a trained model, localisation.py can be used as follows:

python -m interpretability.analyses.localisation \
    --reload last \
    --analysis_config 500_3x3 \
    --explainer_name Ours \
    --smooth 15 \
    --batch_size 64 \
    --save_path "experiments/ImageNet/bcosification/resnet_18/"

• For ViTs, --analysis_config 500_2x2-stride=112 and --striding 112 are required.
• The results along with localisation plots are stored in the localisation_analysis directory automatically created in the experiments directory (--save_path).

For text-localisation using B-cosified CLIP model, text_localisation.py can be used as follows:

on an ImageNet image:

python -m interpretability.analyses.text_localisation \
    --exp_name experiments/ImageNet/clip_bcosification/resnet_50_clip_b2_noBias_randomResizedCrop_sigLip_ImageNet_bcosification \
    --image_index 2 \
    --text_to_localize "green,blue,orange" \
    --save_path /path/to/save

• --use_class_name to localise the class name for a given ImageNet image.
• --save_path is by default set to path provided by --exp_name if not set.

on a random image:

python -m interpretability.analyses.text_localisation \
    --exp_name experiments/ImageNet/clip_bcosification/resnet_50_clip_b2_noBias_randomResizedCrop_sigLip_ImageNet_bcosification \
    --random_img_path /path/to/image \
    --text_to_localize "green,blue,orange"
    --save_path /path/to/save 

• CNNs: resnet18, resnet_50, resnet_50_V1, densenet_121
• ViTs: bcosifyv2_{model_name}_0.001_lrWarmup_gapReorder

{model_name}
    "vitc_ti_patch1_14",
    "vitc_s_patch1_14",
    "vitc_b_patch1_14",
    "vitc_l_patch1_14",
    "simple_vit_ti_patch16_224",
    "simple_vit_s_patch16_224",
    "simple_vit_b_patch16_224",
    "simple_vit_l_patch16_224"

Note: Only b and l models use lrWarmup in the final models.

• CLIP: resnet_50_clip_b2_noBias_randomResizedCrop_sigLip_{dataset}_bcosification; where {dataset} is either ImageNet or CC3M. Also, the base_network for CLIP models is clip_bcosification.

P.S. For more detailed training instructions, please also have a look at TRAINING.md from original B-cos-v2 repository.

The checkpoints for the B-cosified ImageNet CNN and ViT pre-trained models are available here. For B-cosified CLIP pre-trained models, please check here.

• The checkpoints should be renamed to last.ckpt.
• The checkpoints should be placed under the path: ./experiments/{dataset}/{base_network}/{experiment_name}/{model_name}/last.ckpt.

This repository uses code from the following repositories:

• B-cos/B-cos-v2
• openai/CLIP
• mlfoundations/open_clip
• LAION-AI/CLIP_benchmark
• k1rezaei/Text-to-concept

This repository's code is licensed under the Apache 2.0 license which you can find in the LICENSE file.

The pre-trained models are trained on ImageNet (and are hence derived from it), which is licensed under the ImageNet Terms of access, which among others things, only allows non-commercial use of the dataset. It is therefore your responsibility to check whether you have permission to use the pre-trained models for your use case.

Please cite as follows:

@inproceedings{arya2024bcosification,
 author = {Arya, Shreyash and Rao, Sukrut and B\"{o}hle, Moritz and Schiele, Bernt},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
 pages = {62756--62786},
 publisher = {Curran Associates, Inc.},
 title = {B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable},
 url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/72d50a87b218d84c175d16f4557f7e12-Paper-Conference.pdf},
 volume = {37},
 year = {2024}
}