bcos 0.0.1

B-cos models.

B-cos Networks v2

M. Böhle, N. Singh, M. Fritz, B. Schiele.

Improved B-cos Networks.

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

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.

Quick Start

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()

See the demo notebook for more details.

Stay tuned, more info to come soon!