Implicit neural representations (INRs) exhibit exceptional compression and generalisation abilities that have enabled striking progress across a variety of applications. These properties have fuelled a growing interest in leveraging INRs for traditional classification tasks as a memory-efficient alternative representation of images, breaking the persistent link between image resolution and associated resource costs. Current INR classification methods face limitations such as a restriction to low-resolution data and sensitivity to image-space transformations. We attribute these issues to the employed INR architecture which lacks mechanisms for local representation, thereby disregarding spatial structure within the data and furthermore limiting their ability to capture high-frequency details. In this work, we propose ARC: Anchored Representation Clouds, a novel INR architecture that explicitly anchors latent vectors in image-space. By introducing spatial structure to the latent vectors, ARC can capture local image data which in our testing leads to state-of-the-art implicit image classification of both low- and high-resolution images and increased robustness against image-space translation.