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1 PAPER • NO BENCHMARKS YET
Understanding comprehensive assembly knowledge from videos is critical for futuristic ultra-intelligent industry. To enable technological breakthrough, we present HA-ViD – an assembly video dataset that features representative industrial assembly scenarios, natural procedural knowledge acquisition process, and consistent human-robot shared annotations. Specifically, HA-ViD captures diverse collaboration patterns of real-world assembly, natural human behaviors and learning progression during assembly, and granulate action annotations to subject, action verb, manipulated object, target object, and tool. We provide 3222 multi-view and multi-modality videos, 1.5M frames, 96K temporal labels and 2M spatial labels. We benchmark four foundational video understanding tasks: action recognition, action segmentation, object detection and multi-object tracking. Importantly, we analyze their performance and the further reasoning steps for comprehending knowledge in assembly progress, process effici
Hands Guns and Phones (HGP) dataset contains 2199 images (1989 for training an 210 for testing) of people using guns or phones in real-world scenarios (people making phones reviews, shooting drills, or making calls). Every image of this dataset is labeled with the bounding boxes of Hands, Phones and Guns. All the aforementioned images were collected from Youtube videos and have different sizes.
Minor Irrigation Structures Check-Dam Dataset is a public dataset annotated by domain experts using images from Google static map for instance segmentation and object detection tasks.
An object-centric version of Stylized COCO to benchmark texture bias and out-of-distribution robustness of vision models. See the ECCV 22 paper and supplementary material for details.
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STN PLAD is a high-resolution and real-world image dataset of multiple high-voltage power line components. It has 2,409 annotated objects divided into five classes: transmission tower, insulator, spacer, tower plate, and Stockbridge damper, which vary in size (resolution), orientation, illumination, angulation, and background.
1 PAPER • 1 BENCHMARK
TAMPAR is a real-world dataset of parcel photos for tampering detection with annotations in COCO format. For details see the paper and for visual samples the project page. Features are:
The UAVVaste dataset consists to date of 772 images and 3716 annotations. The main motivation for creation of the dataset was the lack of domain-specific data. The datasets that are widely used for object detection evaluation benchmarking. The dataset is made publicly available and is intended to be expanded.
This is the first general Underwater Image Instance Segmentation (UIIS) dataset containing 4,628 images for 7 categories with pixel-level annotations for underwater instance segmentation task
A dataset of all Moroccan money
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The study showed that the apple scab can be detected in the high-resolution RGB images in an early stage of its development. If two datasets, the early and advanced stages, are grouped together, the scab in the early stage is not visible after image resizing for CNN inputs 200-500px.
Plant factories are an advanced form of facility agriculture that enable efficient plant cultivation through controllable environmental conditions, making them highly suitable for the automation and intelligent application of machinery. Tomato cultivation in plant factories has significant economic and agricultural value and can be utilized for various applications such as seedling cultivation, breeding, and genetic engineering. However, manual completion is still required for operations such as detection, counting, and classification of tomato fruits, and the application of machine detection is currently inefficient. Furthermore, research on the automation of tomato harvesting in plant factory environments is limited due to the lack of a suitable dataset. To address this issue, a tomato fruit dataset was constructed for plant factory environments, named as TomatoPlantfactoryDataset, which can be quickly applied to multiple tasks, including the detection of control systems, harvesting