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Object detection in indoor scenes is the task of performing object detection within an indoor environment.
<span style="color:grey; opacity: 0.6">( Image credit: Faster Bounding Box Annotation for Object Detection in Indoor Scenes )</span>
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The SUN RGBD dataset contains 10335 real RGB-D images of room scenes. Each RGB image has a corresponding depth and segmentation map. As many as 700 object categories are labeled. The training and testing sets contain 5285 and 5050 images, respectively. Source: Mix and match networks: multi-domain alignment for unpaired image-to-image translation Image Source: https://rgbd.cs.princeton.edu/
Kitchen Scenes is a multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset. The viewpoints of the scenes are densely sampled and objects in the scenes are annotated with bounding boxes and in the 3D point cloud. Source: Multiview RGB-D Dataset for Object Instance Detection
ISOD contains 2,000 manually labelled RGB-D images from 20 diverse sites, each featuring over 30 types of small objects randomly placed amidst the items already present in the scenes. These objects, typically ≤3cm in height, include LEGO blocks, rags, slippers, gloves, shoes, cables, crayons, chalk, glasses, smartphones (and their cases), fake banana peels, fake pet waste, and piles of toilet paper, among others. These items were chosen because they either threaten the safe operation of indoor mobile robots or create messes if run over. In addition to RGB images, ISOD also includes corresponding depth images and IMU readings. A reference image of each floor type was also recorded using a smartphone. This dataset was used as a real-world validation dataset in the original work to explore the performance of the model beyond synthetic data, specifically focusing on the potential application of real-time robot navigation.
This dataset consists of images of bottles and cups. ### Introduction Dataset consists of images of bottle and cups captured using mobile phones in a real-world scenario. Images were captured under a wide variety of indoor lighting conditions. This dataset can be used for the detection of a wide variety of bottles and cups made up of a variety of materials from a lot of different of viewpoints, locations, orientations, etc. ### Dataset Features - Captured by 3000+ unique users - Captured using mobile phones - Variety of different material of bottles and cups - HD Resolution - Highly diverse - Various lighting conditions - Indoor scenes ### Dataset Features - Classification and detection annotations available - Multiple category annotations possible - COCO, PASCAL VOC and YOLO formats To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform
This dataset is an extremely challenging set of over 5000+ original Electronic Items images captured and crowdsourced from over 1000+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 5000+ - Captured by : Over 1000+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 800+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2021 - Applications : Electronics Detection, Daily item detection, Home Automation, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform
This dataset is an extremely challenging set of over 7000+ original Masks images captured and crowdsourced from over 1200+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs. ### Dataset Features - Dataset size : 7000+ - Captured by : Over 1200+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 900+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2021 - Usage : Mask detection, Mask segregation, Trash Mask detection, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform
This dataset is an extremely challenging set of over 3000+ originally Stair images captured and crowdsourced from over 500+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 3000+ - Captured by : Over 500+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 500+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2022 - Usage : Stair detection , Stair Edge detection , Computer Vision , etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform
This dataset is an extremely challenging set of over 7000+ original Suitcase/Luggage images captured and crowdsourced from over 800+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs. ### Dataset Features - Dataset size : 6000+ - Captured by : Over 1000+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 800+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2021-2022 - Usage : Luggage detection, suitcase detection, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform
This dataset is an extremely challenging set of over 3000+ original Transparent object images such as glasses and mirrors are captured and crowdsourced from over 500+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 3000+ - Captured by : Over 500+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 600+ cities accross India - Diversity : Diversity in object type, lighting, camera type etc. - Device used : Captured using mobile phones in 2020-2022 - Usage : Glass detection | Mirror detection | Transparent Cups detection | Home automation etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform
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