TyreNet: A High-quality Annotated Dataset for Tyre Defect Classification with Deep Learning Models
Description
The dataset consists of a total of 1,698 tyre images. There are 866 images of defective tyres, showcasing a range of issues like cracks, wear patterns, and other anomalies. Additionally, there are 832 images of good (non-defective tyres), mimicking the realistic environment.
Files
Steps to reproduce
The dataset was collected from six different car and bike service stations, each contributing images that replicate various service environments and lighting conditions. Two tyre showrooms also provided images, ensuring a diverse representation of tyre conditions. The inclusion of used but non-defective tyres from cars further adds to the authenticity of the dataset. The images are carefully annotated by experts. The images were captured using a Android smartphone with a 48 MP triple rear camera, ensuring high-resolution and detailed tyre images.