  Using large pre-trained models for image recognition tasks is becoming
increasingly common owing to the well acknowledged success of recent models
like vision transformers and other CNN-based models like VGG and Resnet. The
high accuracy of these models on benchmark tasks has translated into their
practical use across many domains including safety-critical applications like
autonomous driving and medical diagnostics. Despite their widespread use, image
models have been shown to be fragile to changes in the operating environment,
bringing their robustness into question. There is an urgent need for methods
that systematically characterise and quantify the capabilities of these models
to help designers understand and provide guarantees about their safety and
robustness. In this paper, we propose Vision Checklist, a framework aimed at
interrogating the capabilities of a model in order to produce a report that can
be used by a system designer for robustness evaluations. This framework
proposes a set of perturbation operations that can be applied on the underlying
data to generate test samples of different types. The perturbations reflect
potential changes in operating environments, and interrogate various properties
ranging from the strictly quantitative to more qualitative. Our framework is
evaluated on multiple datasets like Tinyimagenet, CIFAR10, CIFAR100 and
Camelyon17 and for models like ViT and Resnet. Our Vision Checklist proposes a
specific set of evaluations that can be integrated into the previously proposed
concept of a model card. Robustness evaluations like our checklist will be
crucial in future safety evaluations of visual perception modules, and be
useful for a wide range of stakeholders including designers, deployers, and
regulators involved in the certification of these systems. Source code of
Vision Checklist would be open for public use.
