## 1 Introduction  
Leather is a widely used material in various industries such as the automotive, fashion, and furniture industries due to its unique texture, durability, and aesthetic appeal. With the global leather market valued at USD 440.64 billion and a projected growth to USD 468.49 billion in 2023 and USD 738.61 billion by 2030 [1], the necessity for optimization and efficiency in all aspects of its production is undeniable. Within the leather manufacturing industry, one of the most important processes involved in transforming raw materials into consumer goods is the inspection process or quality control. By ensuring a high level of consistency in the grading of raw and processed materials, manufacturers not only greatly reduce labour costs, but also eliminate unnecessary material losses caused by the misclassification of exported commodities. Traditional methods for leather surface defect detection rely on qualified technicians to visually inspect every inch of the processed materials. This procedure can be labour-intensive, time-consuming, and highly subjective, which increases the risk of human error and inconsistent defect identification. To overcome these challenges, there is a growing interest in leveraging artificial intelligence (AI) and computer vision techniques for automated and accurate leather surface defect detection. In recent years, vision transformers [2] have been emerging as a powerful approach in computer vision tasks, showing superior performance in image classification [3], object detection [4], and segmentation [5]. Vision transformers are deep neural networks that process images in a patch-based manner, where the image is divided into non-overlapping patches and then linearly embedded into a sequence of vectors. These sequences of vectors are then processed by transformer layers, originally proposed for natural language processing tasks, to capture both local and global contextual information. The self-attention mechanism [6] in transformers allows for capturing long-range dependencies, making them highly effective for analysing complex patterns and structures in images. When implementing such a system in the real-world manufacturing environment, certain practical considerations and potential challenges should be considered. To process images in real-time, a trade-off between model accuracy and speed must be considered. As manufacturers may undergo changes in equipment, surface material types, and/or processing chemicals, the ability to quickly re-train or fine-tune a model on a new dataset can be greatly advantageous. Despite the fact that low-resolution images reduce the computational demands of the model, allowing it to train faster and be applied in a real-time setting, the presence of fewer pixels representing the defective area make them difficult to accurately localise. Defects may be subtle and could potentially be missed or blurred out in a low-resolution image. As the transformer splits the image into patches, if the resolution is too low, each patch might not contain enough detail for the model to make accurate predictions. Furthermore, the manufacturing environment may include variations in lighting, viewing angle, and other factors that might change the appearance of the leather without constituting a defect. The inspection system would therefore require a controlled environment, or a high level of robustness and adaptability from the model. Finally, owing to the reduced availability and feasibility of resources such as high-definition image capturing systems and processing power, many current machine vision-based AI systems are not an option for manufacturers in developing countries, where the majority of raw leather products are produced. For this reason, this research focusses on using anomaly detection and localisation using lower-resolution leather surface images for defect classification. Training an image classifier on a small low-resolution dataset offers several benefits. Firstly, it reduces the hardware and resource requirements for the training process.