HyperKvasir dataset contains 110,079 images and 374 videos where it captures anatomical landmarks and pathological and normal findings. A total of around 1 million images and video frames altogether.
10 PAPERS • 2 BENCHMARKS
InsPLAD is a Dataset for Power Line Asset Inspection containing 10,607 high-resolution Unmanned Aerial Vehicles colour images. It contains 17 unique power line assets captured from real-world operating power lines. Some of those assets (five, to be precise) are also annotated regarding their conditions. They present the following defects: corrosion (4 of them), broken/missing cap (1 of them), and bird's nest presence (1 of them).
4 PAPERS • 1 BENCHMARK
We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual (i.e., anomalous) with respect to the robot’s previous experience in similar environments. These anomalies might indicate unforeseen hazards and, in scenarios where failure is costly, can be used to trigger an avoidance behavior. We contribute three novel image-based datasets acquired in robot exploration scenarios, comprising a total of more than 200k labeled frames, spanning various types of anomalies.
3 PAPERS • NO BENCHMARKS YET
This dataset contains simulated and expert-labelled spectrograms from two radio telescopes: the Hydrogen Epoch of Reionization Array (HERA) in South Africa and the Low-Frequency Array (LOFAR) in the Netherlands. These datasets are intended to test radio-frequency interference (RFI) detection schemes. This entry pertains to the HERA dataset specifically.
2 PAPERS • 1 BENCHMARK
This dataset contains simulated and expert-labelled spectrograms from two radio telescopes: the Hydrogen Epoch of Reionization Array (HERA) in South Africa and the Low-Frequency Array (LOFAR) in the Netherlands. These datasets are intended to test radio-frequency interference (RFI) detection schemes. This entry pertains to the LOFAR dataset specifically.
Scene-focused, multi-modal, episodic data of the images and symbolic world-states seen by an agent completing a pogo-stick assembly task within a video game world. Classes consist of episodes with novel objects inserted. A subset of these novel objects can impact gameplay and agent behavior. Novelty objects can vary in size, position, and occlusion within the images. Usable for novelty detection, generalized category discovery, and class-imbalanced classification.
2 PAPERS • NO BENCHMARKS YET
Multi-pose Anomaly Detection (MAD) dataset, which represents the first attempt to evaluate the performance of pose-agnostic anomaly detection. The MAD dataset containing 4,000+ highresolution multi-pose views RGB images with camera/pose information of 20 shape-complexed LEGO animal toys for training, as well as 7,000+ simulation and real-world collected RGB images (without camera/pose information) with pixel-precise ground truth annotations for three types of anomalies in test sets. Note that MAD has been further divided into MAD-Sim and MAD-Real for simulation-to-reality studies to bridge the gap between academic research and the demands of industrial manufacturing.
COCO-OOC goes beyond standard object detection to ask the question: Which objects are out-of-context (OOC)? Given an image with a set of objects, the goal of COCO-OOC is to determine if an object is inconsistent with the contextual relations, where it must detect the OOC object with a bounding box.
1 PAPER • 1 BENCHMARK
MIAD contains more than 100K high-resolution color images in various outdoor industrial scenarios, designed for unsupervised anomaly detection. This dataset is generated by a 3D graphics software and covers both surface and logical anomalies with pixel-precise ground truth.
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Risholme-2021 contains >3.5K images of strawberries at various growth stages along with anomalous instances. Data collection was performed in the strawberry research farm at the Riseholme campus of the University of Lincoln in UK. For more details, please check out "Homepage" down below.
ADFI Dataset is an image dataset for anomaly detection methods with a focus on industrial inspection. Each category sub dataset comprises a training set of images and a test set of images with various kinds of defects as well as images without defects.
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