Beyond Accuracy: What Matters in Designing Well-Behaved Models?

Robin Hesse^1*, Doğukan Bağcı^1*, Bernt Schiele², Simone Schaub-Meyer^1,3, Stefan Roth^1,3

¹Technical University of Darmstadt ²Max Planck Institute for Informatics, SIC ³hessian.AI

Table of contents

• Table of contents
• News 📰
• Benchmark description 📊
• Interactive plot 📈
• How to install and run the project 🖥️▶️
  • Setting up the environment (conda)
  • How to use the project 🚀
  • Setup choices for starting the experiments ✅
  • How to add and test your own model
• Model zoo 🤖🧠
• Citation

News 📰

• 06.04.2025: Project page is online
• 24.03.2025: Paper and code are released.

Benchmark description 📊

QUBA is an open-source benchmark designed to evaluate different quality dimensions of models with the aim of providing a quality understanding beyond accuracy (QUBA). These quality dimensions include the accuracy, adversarial robustness, corruption robustness, OOD robustness, calibration error, class_balance, object focus, shape bias, and parameters of a given model.

Interactive plot 📈

Explore our data from our experiments with this interactive scatter plot! Easily visualize relationships between quality dimensions and uncover information by hovering and filtering the data points. Dive in and discover new insights!

How to install and run the project 🖥️▶️

Setting up the environment (conda)

In the following it is shown how to set up the environment with conda:

#Choose a folder to clone the repository
git clone .....

#Move to the cloned folder
cd QUBA

#Use the provided environment.yml file to create the conda environment
conda env create -f environment.yml

#activate the environment
conda activate quba

How to use the project 🚀

#After setting up the environment and activating it,
#move into the QUBA folder
cd QUBA

#Before starting the experiments, please specify the directory in which the datasets are located as well as where the helper directory is located. This must be done in the quba_constanty.py file. Only the constants _DATA_DIR and _PROJ_DIR have to be changed for the datasets. 
The quba_constants.py file is located in ./QUBA/helper.
#That folder is used to download the needed datasets.
#The ImageNet Dataset is not included in this function, since you need to apply for it at the official ImageNet Website: (https://image-net.org/download-images).
#The ImageNet-C Dataset is also not included in this function, you can download it at corresponding GitHub Page: (https://github.com/hendrycks/robustness)

#Now you can start the experiments.
#(The following is only an example on how to test a ResNet50 on the metrics
we used for our analysis, please refer to the documentation below for more possibilities)
python evaluate.py --model ResNet50 --params --accuracy --adv_rob --c_rob --ood_rob --object_focus --calibration_error --class_balance --shape_bias --batch_size 32 --file results.xlsx --device cuda:0

#The runtime depends on the device you are using for computation.
#The raw results are stored in the specified excel file, which should
#be in the same folder as the evaluate.py file

Setup choices for starting the experiments ✅

Argument	Explaination
--model	Refers to the model you want to test. Alternatively, instead of testing a single model, you could also models group-wise (e.g. CNN, TRA etc.) or even all at once. The default is ALL
--file	Excel file in which the results are printed after each run. The filename should end with .xlsx. The default value is results.xlsx.
--device	Specifies the device on which the computations are done. Default is cuda:0.
--batch_size	The Batch Size that is used for loading the images. Default is 32.
--num_workers	Number of subprocesses that is used for loading the data. Default is 10.
--accuracy	Measures the Accuracy. (Optional)
--adv_rob	Measures the Adversarial Robustness. (Optional)
--c_rob	Measures the Corruption Robustness. (Optional)
--ood_rob	Measures the OOD Robustness. (Optional)
--object_focus	Measures the Object Focus. (Optional)
--calibration_error	Measures the Calibration Error. (Optional)
--class_balance	Measures the Class Balance. (Optional)
--shape_bias	Measures the Shape Bias. (Optional)
--params	Measures the number of parameters. (Optional)
--compute_corr	Computes the rank correlation between dimensions. This is only possible when evaluating at least two models and at least two quality dimensions (Optional)
--quba_weights	Setting weights for the quba score computation, default is the standard weighting. The weights need to be passed as an array (e.g. [1,2,3,4,5,6,7,8,9]). The custom weights can only be used when all quality dimensions are measured (Optional)

How to add and test your own model

In the example below, it is shown how you can easily add your own model to the QUBA model zoo.

# Add following lines to the load_model(...) function in /helper/generate_data.py
if type == "Name_of_your_Model":
    model= ... #Loading your model
    transform = ... #Specify the image transformations for your model
    return StandardModel(model=model, model_name=model_name, transform=transform)

# In evaluate.py please add your model to the arguments for parsing
parser.add_argument("--model", required=True,
                    choices=[
                              --yourmodel
                             ],
                    help='...')

#Now you can use your model for the experiments

python evaluate.py --model yourmodel --params --accuracy --adv_rob --shape_bias --batch_size 32 --file results.xlsx --device cuda:0

Model zoo 🤖🧠

Our model zoo includes 326 models from the computer vision literature. In the following, we list the sources of the weights for each model.

Source	Models
torchvision	AlexNet, GoogLeNet, VGG11, VGG13, VGG16, VGG19, VGG11-bn, VGG13-bn, VGG16-bn, VGG19-bn, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, WRN-50-2, WRN-101-2, SqueezeNet, InceptionV3, ResNeXt50-32x4d, ResNeXt101-32x8d, ResNeXt101-64x4d, DenseNet121, DenseNet161, DenseNet169, DenseNet201, MobileNetV2, ShuffleNet-v2-05, ShuffleNet-v2-1, ShuffleNet-v2-15, ShuffleNet-v2-2, MobileNetV3-s, MobileNetV3-l, MnasNet-05, MnasNet-075, MnasNet-1, MnasNet-13, EfficientNet-B0, EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B5, EfficientNet-B6, EfficientNet-B7, RegNet-y-400mf, RegNet-y-800mf, RegNet-y-1-6gf, RegNet-y-3-2gf, RegNet-y-8gf, RegNet-y-16gf, RegNet-y-32gf, VIT-b-16, VIT-l-16, VIT-b-32, VIT-l-32, Swin-T, Swin-S, Swin-B, MaxViT-t, SwinV2-T-Win8, SwinV2-S-WIn8, SwinV2-B-Win8, ConvNext-T, ConvNext-S, ConvNext-B, ConvNext-L
PyTorch-image-models	InceptionV4, Inception-ResNetv2, Xception, NasNet-l, MobileNetV3-l-21k, NS-EfficientNet-B0, NS-EfficientNet-B1, NS-EfficientNet-B2, NS-EfficientNet-B3, NS-EfficientNet-B4, NS-EfficientNet-B5, NS-EfficientNet-B6, NS-EfficientNet-B7, BiTM-resnetv2-50x1, BiTM-resnetv2-50x3, BiTM-resnetv2-101x1, BiTM-resnetv2-152x2, EfficientNet-v2-S, EfficientNet-v2-S-21k, EfficientNet-v2-M, EfficientNet-v2-M-21k, EfficientNet-v2-L, EfficientNet-v2-L-21k, DeiT-t, DeiT-s, DeiT-b, ConViT-t, ConViT-s, ConViT-b, CaiT-xxs24, CaiT-xs24, CaiT-s24, CrossViT-9dagger, CrossViT-15dagger, CrossViT-18dagger, XCiT-s24-16, XCiT-m24-16, XCiT-l24-16, LeViT-128, LeViT-256, LeViT-384, PiT-t, PiT-xs, PiT-s, PiT-b, CoaT-t-lite, CoaT-mi-lite, CoaT-s-lite, CoaT-me-lite, MaxViT-b, MaxViT-l, DeiT3-s, DeiT3-s-21k, DeiT3-m, DeiT3-m-21k, DeiT3-b, DeiT3-b-21k, DeiT3-l, DeiT3-l-21k, MViTv2-t, MViTv2-s, MViTv2-b, MViTv2-l, SwinV2-t-W16, SwinV2-s-Win16, SwinV2-b-Win16, SwinV2-b-Win12to16-21k, SwinV2-l-Win12to16-21k, ViT-t5-16, ViT-t5-16-21k, ViT-t11-16, ViT-t11-16-21k, ViT-t21-16, ViT-t21-16-21k, ViT-s-16, ViT-s-16-21k, ViT-b-16-21k, ViT-b-32-21k, ViT-l-16-21k, ViT-l-32-21k, ConvNext-T-21k, ConvNext-S-21k, ConvNext-B-21k, ConvNext-L-21k, BeiT-b, EfficientFormer-l1, EfficientFormer-l3, EfficientFormer-l7, DaViT-t, DaViT-s, DaViT-b, ConvNextV2-N, ConvNextV2-N-21k, ConvNextV2-T, ConvNextV2-T-21k, ConvNextV2-B, ConvNextV2-B-21k, ConvNextV2-L, ConvNextV2-L-21k, EVA02-t-21k, EVA02-s-21k, EVA02-b-21k, InceptionNext-t, InceptionNext-s, InceptionNext-b, FastViT-sa12, FastViT-sa24, FastViT-sa36, SeNet154, ResNet50d, ResNeXt50-32x4d-YFCCM100, ResNet50-yfcc100m, ResNet50-ig1B, ResNeXt101-32x8d-IG1B, ResNeXt50-32x4d-IG1B, ResNet18-IG1B, vit-t-16-21k, EfficientNet-b0-A1, EfficientNet-b1-A1, EfficientNet-b2-A1, EfficientNet-b3-A1, EfficientNet-b4-A1, EfficientNetv2-M-A1, EfficientNetv2-S-A1, RegNety-040-A1, RegNety-080-A1, RegNety-160-A1, RegNety-320-A1, ResNet101-A1, ResNet152-A1, ResNet18-A1, ResNet34-A1, ResNet50-A1, ResNet50d-A1, ResNext50-32x4d-A1, SeNet154-A1, EfficientNet-b0-A2, EfficientNet-b1-A2, EfficientNet-b2-A2, EfficientNet-b3-A2, EfficientNet-b4-A2, EfficientNetv2-M-A2, EfficientNetv2-S-A2, RegNety-040-A2, RegNety-080-A2, RegNety-160-A2, RegNety-320-A2, ResNet101-A2, ResNet152-A2, ResNet18-A2, ResNet34-A2, ResNet50-A2, ResNet50d-A2, ResNext50-32x4d-A2, SeNet154-A2, EfficientNet-b0-A3, EfficientNet-b1-A3, EfficientNet-b2-A3, EfficientNet-b3-A3, EfficientNet-b4-A3, EfficientNetv2-M-A3, EfficientNetv2-S-A3, RegNety-040-A3, RegNety-080-A3, RegNety-160-A3, RegNety-320-A3, ResNet101-A3, ResNet152-A3, ResNet18-A3, ResNet34-A3, ResNet50-A3, ResNet50d-A3, ResNext50-32x4d-A3, SeNet154-A3, RegNet-y-4gf
wielandbrendel	BagNet9, BagNet17, BagNet33
RobustBench	Salman2020Do-RN50-2, Salman2020Do-RN50, Liu2023Comprehensive-Swin-B, Liu2023Comprehensive-Swin-L, Liu2023Comprehensive-ConvNeXt-B, Liu2023Comprehensive-ConvNeXt-L, Singh2023Revisiting-ConvNeXt-T-ConvStem, Singh2023Revisiting-ConvNeXt-S-ConvStem, Singh2023Revisiting-ConvNeXt-B-ConvStem, Singh2023Revisiting-ConvNeXt-L-ConvStem, Singh2023Revisiting-ViT-B-ConvStem, Singh2023Revisiting-ViT-S-ConvStem
Hiera	Hiera-T, Hiera-S, Hiera-B, Hiera-B-Plus, Hiera-L,
Microsoft	BeiTV2-b
FacebookResearchMAE, DINO, DINOv2	vit-b-16-mae-ft, ViT-b-16-DINO-LP, ResNet50-DINO-LP, ViT-s-16-DINO-LP, ViT-l-14-dinoV2-LP, ViT-b-14-dinoV2, ViT-s-14-dinoV2-LP, ViT-l-14-dinov2-reg-LP, ViT-b-14-dinov2-reg-LP, ViT-s-14-dinov2-reg-LP
HuggingFace	siglip-b-16, siglip-l-16, CLIP-B16-DataCompXL, CLIP-B16-Laion2B, CLIP-B16-CommonPool-XL-DFN2B, CLIP-L14-OpenAI, CLIP-L14-DataCompXL, CLIP-L14-Laion2B, CLIP-L14-CommonPool-XL-DFN2B, ViT-B-16-SigLIP2, ViT-L-16-SigLIP2-256, CLIP-B16-V-OpenAI, CLIP-B16-V-Laion2B, CLIP-B32-V-OpenAI, CLIP-B32-V-Laion2B
OpenAI	clip-resnet50, clip-vit-b-16, clip-resnet101, clip-vit-b-32
Apple	mobileclip-s0, mobileclip-s1, mobileclip-s2, mobileclip-b, mobileclip-blt
moboehle	bcos-convnext-base, bcos-convnext-tiny, bcos-DenseNet121, bcos-DenseNet161, bcos-DenseNet169, bcos-DenseNet201, bcos-ResNet152, bcos-ResNet18, bcos-ResNet34, bcos-ResNet50, bcos-simple-vit-b-patch16-224, bcos-ResNet101
OpenCLIP	metaclip-b16, convnext-large-d-clip, metaclip-l14, convnext-base-w-320-clip, convnext-large-d-320-clip
Trained by us. Checkpoints will be published upon the acceptance of the paper	Hiera-B-LP, Hiera-S-LP, Hiera-T-LP, ViTB-DINO-FT, ResNet50-DINO-FT, vit-b-16-mae-lp, ViT-l-14-dinoV2-FT, ViT-b-14-dinoV2-FT, ViT-s-14-dinoV2-FT, ViT-l-14-dinoV2-FT-Reg, ViT-b-14-dinoV2-FT-Reg, ViT-s-14-dinoV2-FT-Reg

You can list all available models and model groups by calling the list_models() function in /helper/generate_data.py

Citation

If you find this project useful, please consider citing:

@article{Hesse:2025:beyond_accuracy,
    title={Beyond Accuracy: What Matters in Designing Well-Behaved Models?}, 
    author={Robin Hesse and Do\u{g}ukan Ba\u{g}c\i and Bernt Schiele and Simone Schaub-Meyer and Stefan Roth},
    year={2025},
    journal={arXiv:2503.17110 [cs.CV]},
    }