The GoodsAD dataset contains 6124 images with 6 categories of common supermarket goods. Each category contains multiple goods. All images are acquired with 3000 × 3000 high-resolution. The object locations in the images are not aligned. Most objects are in the center of the images and one image only contains a single object. Most anomalies occupy only a small fraction of image pixels. Both image-level and pixel-level annotations are provided.
Each image is named with 6 digits, with the first three digits representing the category of the product and the last three representing the serial number. The dataset format is same as MVTec AD.
Variants: GoodsAD
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Anomaly Classification | MiniMaxAD-fr | MiniMaxAD: A Lightweight Autoencoder for … | 2024-05-16 |
| Anomaly Classification | SimpleNet | SimpleNet: A Simple Network for … | 2023-03-27 |
| Anomaly Classification | RD4AD | Anomaly Detection via Reverse Distillation … | 2022-01-26 |
| Anomaly Classification | NSA | Natural Synthetic Anomalies for Self-Supervised … | 2021-09-30 |
| Anomaly Classification | DRAEM | DRAEM -- A discriminatively trained … | 2021-08-17 |
| Anomaly Classification | CFLOW-AD | CFLOW-AD: Real-Time Unsupervised Anomaly Detection … | 2021-07-27 |
| Anomaly Classification | PatchCore-100% | Towards Total Recall in Industrial … | 2021-06-15 |
| Anomaly Classification | PatchCore-1% | Towards Total Recall in Industrial … | 2021-06-15 |
| Anomaly Classification | CutPaste | CutPaste: Self-Supervised Learning for Anomaly … | 2021-04-08 |
| Anomaly Classification | SPADE | Sub-Image Anomaly Detection with Deep … | 2020-05-05 |
Recent papers with results on this dataset: