Benchmarking the Attribution Quality of Vision Models

R. Hesse, S. Schaub-Meyer, and S. Roth. Benchmarking the Attribution Quality of Vision Models. NeurIPS Datasets and Benchmarks Track, 2024.

Paper | ArXiv | Video (TBD) | Poster

Evaluation

In-Domain Single Deletion Score

To evaluate an attribution method on a model, you can run the following command:

python evaluate.py --evaluation_protocol single_deletion --grid_rows_and_cols 4 --data_dir /datasets/imagenet --model your_model --pretrained True --pretrained_ckpt /data/rhesse/phd_imagenet_patches_evaluation/checkpoints/resnet50_imagenet1000_lr0.001_epochs30_step10_osdi_checkpoint_best.pth.tar --explainer your_explainer --batch_size 128

You just need to specify the --data_dir (the path to ImageNet), the --model (e.g., resnet50,vgg16, or vit_base_patch16_224), the --pretrained_ckpt (see table with model weights), and the --explainer (see table with attribution methods). Please see the code in evaluate.py for additional details for all parameters.

Single Deletion Score

The single deletion protocol works similar to our proposed in-domain single deletion score but without fine-tuning the model. Thus, you can use the same commands as before, simply specifying --pretrained True --pretrained_ckpt none. For models that do not automatically download the pre-trained ImageNet weights you need to download them and specify them with --pretrained_ckpt path_to_weights.

Incremental Deletion Score

To run the indremental deletion protocol, run the following command:

python evaluate.py --evaluation_protocol incremental_deletion --id_baseline zeros --id_steps 32 --id_order ascending --data_dir /fastdata/rhesse/datasets/imagenet --model your_model --workers 0 --pretrained True --pretrained_ckpt none --seed 0 --nr_images 4096 --explainer your_explainer --batch_size 128

Set --id_update_attribution True to update the attribution in each deletion step.

Attribution Methods

The following table provides an overview of the evaluated attribution methods and how to set the parameters to use them. If you want to add your own attribution method, you need to add the method in /explainers, implement your explainer wrapper in /explainers/explainer_wrapper.py, add the name of you method to the parameter list in evaluate.py, and instanciate your attribution method in evaluate.py when its name is used as the explainer parameter.

Parameter	Name
--explainer IxG	InputXGradient
--explainer IxG-SG	InputXGradient + SmoothGrad
--explainer IG	Integrated Gradients (zero baseline)
--explainer IG-U	Integrated Gradients (uniform baseline)
--explainer IG-SG	Integrated Gradients (zero baseline) + SmoothGrad
--explainer IxG --attribution_transform abs	InputXGradient (absolute)
--explainer IxG-SG --attribution_transform abs	InputXGradient + SmoothGrad (absolute)
--explainer IG --attribution_transform abs	Integrated Gradients (zero baseline) (absolute)
--explainer IG-U --attribution_transform abs	Integrated Gradients (uniform baseline) (absolute)
--explainer IG-SG --attribution_transform abs	Integrated Gradients (zero baseline) + SmoothGrad (absolute)
--explainer IG-SG-SQ --attribution_transform abs	Integrated Gradients (zero baseline) + SmoothGrad (squared)
--explainer RISE --attribution_transform abs	RISE (zero baseline)
--explainer RISE-U --attribution_transform abs	RISE (uniform baseline)
--explainer Grad-CAM	Grad-CAM (CNN only)
--explainer Grad-CAMpp	Grad-CAM++ (CNN only)
--explainer SG-CAMpp	Grad-CAM++ + SmoothGrad (CNN only)
--explainer XG-CAM	XGrad-CAM (CNN only)
--explainer Layer-CAM	Layer-CAM (CNN only)
--explainer Rollout	Rollout (ViT only)
--explainer CheferLRP	CheferLRP (ViT only)
--explainer Bcos	Bcos (Bcos only)
--explainer BagNet	BagNet (BagNet only)

Models

If you want to add your own models, you need to add the model file in /models, implement your model wrapper in /models/model_wrapper.py, add the name of you model to the parameter list in evaluate.py, and load your model in evaluate.py when that name is used as model parameter.

Downloads

The following table provides the model weights for the fine-tuned models needed for our in-domain single deletion score.

Model	Parameter	Download
ResNet-18	--model resnet18	resnet18
ResNet-50	--model resnet50	resnet50
ResNet-101	--model resnet101	resnet101
ResNet-152	--model resnet152	resnet152
Wide ResNet-50	--model wide_resnet50_2	wide_resnet50_2
ResNet-50 w/o BatchNorm	--model fixup_resnet50	fixup_resnet50
ResNet-50 w/o BatchNorm w/o bias	--model x_resnet50	x_resnet50
VGG-11	--model vgg11	vgg11
VGG-13	--model vgg13	vgg13
VGG-16	--model vgg16	vgg16
VGG-19	--model vgg19	vgg19
VGG-16 w/ BatchNorm	--model vgg16_bn	vgg16_bn
VGG-16 w/o BatchNorm w/o bias	--model x_vgg16	x_vgg16
ViT-B-16	--model vit_base_patch16_224	vit_base_patch16_224
Bcos-ResNet-50	--model bcos_resnet50	bcos_resnet50
BagNet-33	--model bagnet33	bagnet33

Training

If you want to train your own models, you can follow the steps described in the beginning of this section and run the following command:

python train.py --data_dir /datasets/imagenet --model your_model --lr 0.001 --pretrained --store_path /checkpoints/ --checkpoint_prefix your_model_imagenet1000_lr0.001_epochs30_step10

Citation

If you find our work helpful, please consider citing

@inproceedings{Hesse:2024:IDSDS,
  title     = {Benchmarking the Attribution Quality of Vision Models},
  author    = {Hesse, Robin and Schaub-Meyer, Simone and Roth, Stefan},
  booktitle = {NeurIPS},
  year      = {2024},
}