태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Open World Object Detection is a computer vision problem where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.
PASCAL VOC 2007 is a dataset for image recognition. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor The dataset can be used for image classification and object detection tasks. Image Source: Object Detection and Recognition in Images
UVO is a new benchmark for open-world class-agnostic object segmentation in videos. Besides shifting the problem focus to the open-world setup, UVO is significantly larger, providing approximately 8 times more videos compared with DAVIS, and 7 times more mask (instance) annotations per video compared with YouTube-VOS and YouTube-VIS. UVO is also more challenging as it includes many videos with crowded scenes and complex background motions. Some highlights of the dataset include: - High quality instance masks densely annotated at 30 fps on 1024 YouTube videos and 1fps on 10337 videos from Kinetics dataset - Open-world: annotating all objects in each video, 13.5 objects per video on average - Diverse object categories: 57% of objects are not covered by COCO categories
COCO-Mixed dataset includes 897 images with annotations of both known and unknown categories. It contains 2533 unknown objects and 2658 known objects, with original COCO annotations used as labels for known objects. Unambiguous unlabeled objects are also annotated. The dataset is more challenging to evaluate due to the images containing more object instances with complex categories and concentrated locations.
COCO-OOD dataset contains only unknown categories, consisting of 504 images with fine-grained annotations of 1655 unknown objects. All annotations consist of original annotations in COCO and the augmented annotations on the basis of the COCO definition.
This work contributes a large, complex, and realistic high-quality safety clothing and helmet detection (SFCHD) dataset. The dataset comprises 12,373 images, covering 7 categories, with a total of 50,558 labeled instances. All images are captured from factory surveillance cameras, encompassing 40 different scenes across two chemical plants. It is worth noting that our SFCHD dataset not only provides a rich set of training samples but also serves as a benchmark for the evaluation of various detection tasks, such as small object detection, and high-low light object detection.
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