Search Results for class:object-4
Multimodal
1.08k images
object1111 and has 2 level of gingivitis 2 at position 2111 and has 2 level of gingivitis 3 at position 1211 and has 4 level of gingivitis 1 at position 1311 and has 4 level of gingivitis 3 at position 1211 and has 4 level of gingivitis 3 at position 1311 and has 4 level of gingivitis 4 at position 1211 in upper jaw and teeth11 in upper jaw and teeth has 2 level of gingivitis 1 at position 4111 in upper jaw and teeth has 2 level of gingivitis 2 at position 4111 in upper jaw and teeth has 2 level of gingivitis 2 at position 4211 in upper jaw and teeth has 3 level of gingivitis 2 at position 4211 in upper jaw; Level 3 gingivitis teeth 1311 is non-inflamed at position and has teeth 1311 level of gingivitis 1 and has teeth 2111 level of gingivitis 2 and has teeth 1211 level of gingivitis 2 and has teeth 1311 level of gingivitis 3 and has teeth 1212
Object Detection
5.98k images·1
handobject==============================Pen - v4 2023-07-10 2:04pmRoboflow is an end-to-end computer vision platform that helps youTablet - v1 2023-09-21 6:23pmThis dataset was exported via roboflow.com on September 4, 2023 at 6:21 AM GMTbook - v12 2023-08-01 10:47amphone model - v6 2023-09-01 11:35pm
Object Detection
1.02k images·2 models·16
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Classification
310 images
<?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Afghan_11172-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>386<-width> <height>500<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Afghan_hound<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>77<-xmin> <ymin>190<-ymin> <xmax>370<-xmax> <ymax>433<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Afghan_3531-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>383<-width> <height>500<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Afghan_hound<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>205<-xmin> <ymin>71<-ymin> <xmax>331<-xmax> <ymax>411<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Afghan_357-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>368<-width> <height>500<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Afghan_hound<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>96<-xmin> <ymin>82<-ymin> <xmax>280<-xmax> <ymax>476<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Afghan_5559-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>342<-width> <height>500<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Afghan_hound<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>103<-xmin> <ymin>211<-ymin> <xmax>301<-xmax> <ymax>423<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>African_2192-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>500<-width> <height>333<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>African_hunting_dog<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>1<-xmin> <ymin>11<-ymin> <xmax>499<-xmax> <ymax>331<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>African_2435-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>350<-width> <height>250<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>African_hunting_dog<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>0<-xmin> <ymin>28<-ymin> <xmax>320<-xmax> <ymax>227<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>African_5683-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>500<-width> <height>333<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>African_hunting_dog<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>144<-xmin> <ymin>58<-ymin> <xmax>362<-xmax> <ymax>314<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>African_8037-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>500<-width> <height>333<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>African_hunting_dog<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>100<-xmin> <ymin>6<-ymin> <xmax>450<-xmax> <ymax>332<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Airedale_1736-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>200<-width> <height>226<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Airedale<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>34<-xmin> <ymin>1<-ymin> <xmax>188<-xmax> <ymax>206<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Airedale_3500-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>300<-width> <height>250<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Airedale<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>10<-xmin> <ymin>21<-ymin> <xmax>272<-xmax> <ymax>239<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Airedale_3879-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>358<-width> <height>500<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Airedale<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>45<-xmin> <ymin>27<-ymin> <xmax>321<-xmax> <ymax>491<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Airedale_5048-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>500<-width> <height>369<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Airedale<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>14<-xmin> <ymin>103<-ymin> <xmax>424<-xmax> <ymax>357<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Airedale_7790-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>334<-width> <height>500<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Airedale<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>28<-xmin> <ymin>133<-ymin> <xmax>332<-xmax> <ymax>498<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Airedale_8826-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>500<-width> <height>375<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Airedale<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>75<-xmin> <ymin>112<-ymin> <xmax>416<-xmax> <ymax>250<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>American_9797-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>500<-width> <height>414<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>American_Staffordshire_terrier<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>6<-xmin> <ymin>47<-ymin> <xmax>498<-xmax> <ymax>412<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Appenzeller_2666-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>500<-width> <height>375<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Appenzeller<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>0<-xmin> <ymin>45<-ymin> <xmax>372<-xmax> <ymax>374<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Appenzeller_2913-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>500<-width> <height>333<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Appenzeller<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>72<-xmin> <ymin>16<-ymin> <xmax>402<-xmax> <ymax>287<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Appenzeller_3971-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>500<-width> <height>335<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Appenzeller<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>142<-xmin> <ymin>45<-ymin> <xmax>499<-xmax> <ymax>276<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Appenzeller_7392-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>400<-width> <height>481<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Appenzeller<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>115<-xmin> <ymin>38<-ymin> <xmax>344<-xmax> <ymax>429<-ymax> <-bndbox> <-object> <-annotation><?xml version=1-0 encoding=utf-8?> <annotation> <folder -> <filename>Australian_1536-jpg<-filename> <source> <database>ImageNet database<-database> <-source> <size> <width>200<-width> <height>194<-height> <depth>3<-depth> <-size> <segment>0<-segment> <object> <name>Australian_terrier<-name> <pose>Unspecified<-pose> <truncated>0<-truncated> <difficult>0<-difficult> <bndbox> <xmin>0<-xmin> <ymin>5<-ymin> <xmax>199<-xmax> <ymax>193<-ymax> <-bndbox> <-object> <-annotation>
Object Detection
35 images
boilerdoorwindow3 WAY MOTORIZED CONTROL VALVEADJUSTABLE BEDAHUAIR CIRCUIT BREAKER DRAWOUT TYPEAIR CIRCUIT BREAKER NON DRAWOUT TYPE SERIES TRIPAIR CONDITIONING CONTROLLERAIR FILTERAIR GRILLANGLE GLOBE VALVEAREA DRAIN 300X300MMARROWASSEMBLY POINTATHLETIC GROUND 1ATHLETIC GROUND 2ATHLETIC GROUND 3ATHLETIC GROUND 4AUTOMATIC AIR VENT WITH ISOLATION VALVE
Object Detection
2.51k images·1 model
aircraftairplaneairportbridgechimneyharborshipvehicle(x1,y1),(x2,y2),aExpressway-Service-areaExpressway-toll-stationGong Cheng, Junwei Han, Peicheng Zhou, Lei Guo. Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98: 119-132, 2014.Gong Cheng, Junwei Han. A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117: 11-28, 2016.Please cite the following relevant papers when publishing results that use this dataset fully or partly:The folder "ground truth" contains 650 separate text files and each one corresponds to an image in "positive image set" folder. Each line of those text files defines a ground truth bounding box in the following format:These images were cropped from Google Earth and Vaihingen data set and then manually annotated by experts. The Vaihingen data was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF): http://www.ifp.uni-stuttgart.de/dgpf/DKEPAllg.html.This dataset contains totally 800 VHR remote sensing images, where the folder "negative image set" includes 150 images that do not contain any targets of the given object classes and the folder "positive image set" includes 650 images with each image containing at least one target to be detected.This is a 10-class geospatial object detection dataset used for research purposes only.These ten classes of objects are airplane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, and vehicle.This very-high-resolution (VHR) remote sensing image dataset was constructed by Dr. Gong Cheng et al. from Northwestern Polytechnical University (NWPU).baseballfield
Classification
1.73k images
object2 0-9598958333333333 0-44722222222222224 1 0-44722222222222224 1 1 0-9598958333333333 12 0-9598958333333333 0-44722222222222224 1 0-44722222222222224 1 1 0-9598958333333333 1 3 0 0 1 0 1 1 0 1 42 0-9598958333333333 0-44722222222222224 1 0-44722222222222224 1 1 0-9598958333333333 1 3 0 0 1 0 1 1 0 1 4 52 0-9598958333333333 0-44722222222222224 1 0-44722222222222224 1 1 0-9598958333333333 1 3 0 0 1 0 1 1 0 1 62 0-9598958333333333 0-44722222222222224 1 0-44722222222222224 1 1 0-9598958333333333 1 42 0-9598958333333333 0-44722222222222224 1 0-44722222222222224 1 1 0-9598958333333333 1 4 52 0-9598958333333333 0-44722222222222224 1 0-44722222222222224 1 1 0-9598958333333333 1 52 0-9598958333333333 0-44722222222222224 1 0-44722222222222224 1 1 0-9598958333333333 1 5 62 0-9598958333333333 0-44722222222222224 1 0-44722222222222224 1 1 0-9598958333333333 1 63 0 0 1 0 1 1 0 13 0 0 1 0 1 1 0 1 4 5 63 0 0 1 0 1 1 0 1 53 0 0 1 0 1 1 0 1 63 0 0 1 0 1 1 0 1 744 5567
