B-cos Networks v2

DOI | arXiv | code

B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers

Moritz Böhle, Navdeeppal Singh, Mario Fritz, Bernt Schiele. TPAMI, 2024.

Table of Contents

• Introduction
• Quick Start
• Installation
  • bcos Package
  • Training Environment Setup
    • Setting Data Paths
• Usage
  • Evaluation
  • Training
• Model Zoo
• License

Introduction

This repository contains the code for the B-cos v2 models.

These models are more efficient and easier to train than the original v1 B-cos models. Furthermore, we make a large number of pretrained B-cos models available for use.

If you want to take a quick look at the explanations the models generate, you can try out the Gradio web demo on .

If you prefer a more hands-on approach, you can take a look at the demo notebook on Colab or load the models directly via torch.hub as explained below.

If you simply want to copy the model definitions, we provide a minimal, single-file reference implementation including explanation mode in extra/minimal_bcos_resnet.py!

UPDATE: We have also released our ViT models! See Model Zoo.

Quick Start

You only need to make sure you have torch and torchvision installed.

Then, loading the models via torch.hub is as easy as:

import torch

# list all available models
torch.hub.list('B-cos/B-cos-v2')

# load a pretrained model
model = torch.hub.load('B-cos/B-cos-v2', 'resnet50', pretrained=True)

Inference and explanation visualization is as simple as:

from PIL import Image
import matplotlib.pyplot as plt

# load image
img = model.transform(Image.open('cat.jpg'))
img = img[None].requires_grad_()

# predict and explain
model.eval()
expl_out = model.explain(img)
print("Prediction:", expl_out["prediction"])  # predicted class idx
plt.imshow(expl_out["explanation"])
plt.show()

Each of the models has its inference transform attached to it, accessible via model.transform. Furthermore, each model has a .explain() method that takes an image tensor and returns a dictionary containing the prediction and the explanation, and some extras.

See the demo notebook for more details on the .explain() method.

Furthermore, each model has a get_classifier and get_feature_extractor method that return the classifier and feature extractor modules respectively. These can useful for fine-tuning the models!

Installation

Depending on your use case, you can either install the bcos package or set up the development environment for training the models (for your custom models or for reproducing the results).

bcos Package

If you are simply interested in using the models (pretrained or otherwise), then we provide a bcos package that can be installed via pip:

pip install bcos

This contains the models, their modules, transforms, and other utilities making it easy to use and build B-cos models. Take a look at the public API here. (I'll add a proper docs site if I have time or there's enough interest. Nonetheless, I have tried to keep the code well-documented, so it should be easy to follow.)

Training Environment Setup

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

Using conda (recommended, especially if you want to reproduce the results):

conda env create -f environment.yml
conda activate bcos

Using pip

pip install -r requirements-train.txt

Setting Data Paths

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

1. DATA_ROOT
2. IMAGENET_PATH

to the paths of the data directories.

The DATA_ROOT environment variable should point to the data root directory for CIFAR-10 (will be automatically downloaded). For ImageNet, the IMAGENET_PATH environment variable should point to the directory containing the train and val directories.

Usage

For the bcos package, as mentioned earlier, take a look at the public API here.

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

Evaluation

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

python evaluate.py --dataset ImageNet --hubconf resnet18

This will download the model from torch.hub and evaluate it on the ImageNet validation set. The default batch size is 1, but you can change it using the --batch-size argument. Replace resnet18 with any of the other models listed in Model Zoo that you wish to evaluate.

Training

Short version:

python train.py \
  --dataset ImageNet \
  --base_network bcos_final \
  --experiment_name resnet18

Long version: See TRAINING.md for more details on how the setup works and how to train your own models.

Model Zoo

Here are the ImageNet pre-trained models available in the model zoo. You can find the links to the model weights below (uploaded to the Weights GitHub release).

Model/Entrypoint	Top-1 Accuracy	Top-5 Accuracy	#Params	Download
resnet18	68.736%	87.430%	11.69M	link
resnet34	72.284%	90.052%	21.80M	link
resnet50	75.882%	92.528%	25.52M	link
resnet101	76.532%	92.538%	44.50M	link
resnet152	76.484%	92.398%	60.13M	link
resnext50_32x4d	75.820%	91.810%	25.00M	link
densenet121	73.612%	91.106%	7.95M	link
densenet161	76.622%	92.554%	28.58M	link
densenet169	75.186%	91.786%	14.08M	link
densenet201	75.480%	91.992%	19.91M	link
vgg11_bnu	69.310%	88.388%	132.86M	link
convnext_tiny	77.488%	93.192%	28.54M	link
convnext_base	79.650%	94.614%	88.47M	link
convnext_tiny_bnu	76.826%	93.090%	28.54M	link
convnext_base_bnu	80.142%	94.834%	88.47M	link
densenet121_long	77.302%	93.234%	7.95M	link
resnet50_long	79.468%	94.452%	25.52M	link
resnet152_long	80.144%	94.116%	60.13M	link
simple_vit_ti_patch16_224	59.960%	81.838%	5.80M	link
simple_vit_s_patch16_224	69.246%	88.096%	22.28M	link
simple_vit_b_patch16_224	74.408%	91.156%	86.90M	link
simple_vit_l_patch16_224	75.060%	91.378%	178.79M	link
vitc_ti_patch1_14	67.260%	86.774%	5.32M	link
vitc_s_patch1_14	74.504%	91.288%	20.88M	link
vitc_b_patch1_14	77.152%	92.926%	81.37M	link
vitc_l_patch1_14	77.782%	92.966%	167.44M	link
standard_simple_vit_ti_patch16_224	70.230%	89.380%	5.67M	link
standard_simple_vit_s_patch16_224	74.470%	91.226%	21.96M	link
standard_simple_vit_b_patch16_224	75.300%	91.026%	86.38M	link
standard_simple_vit_l_patch16_224	75.710%	90.050%	178.10M	link
standard_vitc_ti_patch1_14	72.590%	90.788%	5.33M	link
standard_vitc_s_patch1_14	75.756%	91.994%	20.91M	link
standard_vitc_b_patch1_14	76.790%	92.024%	81.39M	link
standard_vitc_l_patch1_14	77.866%	92.298%	167.54M	link

You can find these entrypoints in bcos/models/pretrained.py.

License

This repository's code is licensed under the Apache License 2.0 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.

Citation

@article{Boehle2024TPAMI,
  author={Böhle, Moritz and Singh, Navdeeppal and Fritz, Mario and Schiele, Bernt},
  title = {B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2024},
  pages = {1-15},
  doi = {10.1109/TPAMI.2024.3355155},
}