Top Size Datasets and Models
The datasets below can be used to train fine-tuned models for size detection. You can explore each dataset in your browser using Roboflow and export the dataset into one of many formats.
Instance Segmentation
2.89k images
Object Detection
1.73k images
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
942 images·1 model
chocolateChewy-Fruit-Sticks-CinnamonDonut-Pop-Angel-Cream-BallDonut-Pop-Chocolate-Fashion-BallDonut-Pop-Coconut-Chocolate-BallDonut-Pop-Golden-Chocolate-BallDonut-Pop-Old-Fashioned-BallDonut-Pop-Pon-de-Strawberry-BallDonut-Pops-16piecesDonut-Pops-24piecesDonut-Pops-8piecesFluffy-baked-doughnuts-plain-mini-size-2piecesHot-Savory-Pie-BBQ-FrankfurtHot-Sweet-Pie-AppleMochiri-Fruit-Stick-HoneyMoist-chocolate-muffinMoist-muffins-with-butter-flavorShrimp-gratin-pieZaku -Mochi-Dog-CurryZaku-Mochi-Dog-Egg
Classification
5k images·1 model
'mongst nonemigrant ecclesia9th sergelim DelaplaineA&P Hordville siliconsAA mesmerism chorioadenomaACWP hypohepatia hedginglyAIDS off-bitten retroactivelyASTMS brewst rehumiliatedATP2 flagellations green-fishAbercrombie jemidars choristerAbruzzi forehock LampsilisAcacea quatral enhancersAcalypterae eyewinker DominoAcanthodes calyceal tubectomyAcanthodidae elastics paleoanthropologyAcolhua Hammondsport guttulateAdapis PIO LyssaAdel endover chequeredAdventism discanonized hayliftAeria braveries coelomicAfridi whoosh mycol
Object Detection
2.86k images
boxAlmond ToscaniAltic TraditionalArdoma Chopped TomatoesArnotts PremeirAspora Clear Regular StrengthBetta Natural Gold conesBoags St GeorgeBrita Filter CatridgesBrita Marella CoolButter MentholCD_RWCadbury Dairy MilkCadbury RosesCarbonellCenovis Womens MultiChocolate Flavoured BiscuitsColes Beans Red KidneyColes Farmland Beans Mexican ChilliColes Reliance
Classification
2.23k images·1 model·10
CRUNCH PATTEXTRA CRUNCHY SKIPPY Creamy-Peanut ButterEXTRA CRUNCHY SKIPPY Peanut ButterGhana RedMALTESERSMEIJI APOLLO STRAWBERRY CHOCOLATEMILKYWAYM_M FUN SIZENERDS GRAPENERDS STRAWBERRYSAFARI CARAMEL CRISPSKITTLES CRAZY SOURSSuper Delights Brownie BitesSuper Delights Butterscotch BitesTOFILUK CARAMEL CRUNCH
Object Detection
9.91k images·1 model·12
3-Way Ball Valve3-Way General Regulating Valve3-Way General Valve3-Way Globe Regulating Valve3-Way Globe Valve3-Way Plug Valve4-Way General Valve4-Way Globe Valve4-Way Plug Valve45deg_TeeAir CoolerAlfa Laval Heat ExchangerAlfa Laval PlatesAngle General Regulating ValveAngle General ValveAngle Globe Regulating ValveAngle Globe ValveBEU TEMABaffle 60DegBaffle 90Deg
Object Detection
1.53k images·11 models
cabinetdoorgatelabelsinktoiletwallwindow- understand and search unstructured image data- use active learning to improve your dataset over time.L stairs with landingQQQQQQQQQQQQQQRoboflow is an end-to-end computer vision platform that helps youRoomRoom with size markingsabath rubbath tubbathroom with tub and sink
Object Detection
236 images
Object Detection
300 images·5
Almond ToscaniAltic TraditionalArdoma Chopped TomatoesArnotts PremeirAspora Clear Regular StrengthBetta Natural Gold conesBoags St GeorgeBrita Filter CatridgesBrita Marella CoolButter MentholCD_RWCadbury Dairy MilkCadbury RosesCarbonellCenovis Womens MultiChocolate Flavoured BiscuitsColes Beans Red KidneyColes Farmland Beans Mexican ChilliColes RelianceColes Waffel Cones
Instance Segmentation
9.31k images
Object Detection
254 images
Beyti Full Cream Milk 900mLBisco Misr Luxe Original Plain Biscuits Extra sizeBisco Misr Max Tea BiscuitsBisco Misr Wafer Vanilla BigCrystal Sunflower Oil - 800mLDomty White Cheese with Olive FlavourEl Maleka Elbow Pasta - 400GFa Men Anti-perspirant Roll-onHeinz Tomato Ketchup GrillHeinz Tomato PasteIndomie Instant Noodles Supermi VegetableKnorr Vegetable Stock - 12 CubesLipton yellow label tea dust 40gMalh Baladna Salt - 300gMena Foul Fava BeansNivea Soft Moisturizing CreamOniro Teabix BiscuitsRehana Flour 1KgSavannah Classic Soap - 125gShepsy Salt and Vinegar Chips
Classification
200 images
https:--as1-ftcdn-net-v2-jpg-00-25-13-90-1000_F_25139076_p3arEf42fFq7T95JIImqgAhWTCFiF39u-jpghttps:--as1-ftcdn-net-v2-jpg-02-04-31-78-1000_F_204317824_B35DtLay5hEVFkBaC4a1TOL9Mo0kxtoy-jpghttps:--as2-ftcdn-net-v2-jpg-01-38-56-55-1000_F_138565592_hTAXcY466IEuJOVB5xUH54nrPmbZYMn5-jpghttps:--as2-ftcdn-net-v2-jpg-02-38-45-27-1000_F_238452704_ZKlnIKazn4KxagIoU2qLA9P1iT0U4SnT-jpghttps:--c8-alamy-com-comp-2AWX8CT-full-length-portrait-of-a-man-with-arms-folded-2AWX8CT-jpghttps:--c8-alamy-com-comp-2D989TC-pensive-man-standing-full-length-folded-arms-and-crossed-legs-thinking-barefoot-male-model-in-white-shirt-demonstrates-his-emotions-while-posing-on-2D989TC-jpghttps:--c8-alamy-com-comp-2K2ANYM-mature-business-man-standing-with-arms-crossed-full-length-of-handsome-mature-business-man-standing-with-arms-crossed-on-white-background-2K2ANYM-jpghttps:--c8-alamy-com-comp-2M7HRWB-beaming-with-confidence-a-beautiful-young-woman-standing-with-her-arms-folded-smiling-at-the-camera-full-length-2M7HRWB-jpghttps:--c8-alamy-com-comp-2PT9BCK-im-always-ready-for-the-next-challenge-full-length-shot-of-a-handsome-young-man-standing-alone-in-the-studio-with-his-arms-folded-2PT9BCK-jpghttps:--c8-alamy-com-comp-2WG1N4G-a-man-full-length-on-a-white-background-crossed-his-arms-2WG1N4G-jpghttps:--c8-alamy-com-comp-2WG3JAY-a-man-full-length-on-a-white-background-crossed-his-arms-2WG3JAY-jpghttps:--c8-alamy-com-comp-2WJYN9Y-a-man-full-length-on-a-white-background-crossed-his-arms-2WJYN9Y-jpghttps:--c8-alamy-com-comp-2WYWYXP-full-length-portrait-of-a-fit-young-man-posing-with-folded-arms-isolated-on-white-background-2WYWYXP-jpghttps:--c8-alamy-com-comp-C1HFNH-serious-young-man-standing-full-body-with-arms-crossed-isolated-on-C1HFNH-jpghttps:--c8-alamy-com-comp-CF847X-young-male-standing-with-arms-folded-CF847X-jpghttps:--c8-alamy-com-comp-CTY99C-a-man-standing-with-his-arms-crossed-CTY99C-jpghttps:--c8-alamy-com-comp-CTYAY1-a-man-standing-with-his-arms-crossed-CTYAY1-jpghttps:--c8-alamy-com-comp-D9G044-woman-standing-with-her-arms-crossed-D9G044-jpghttps:--c8-alamy-com-comp-DJC9GY-athletic-young-male-standing-with-arms-folded-DJC9GY-jpghttps:--c8-alamy-com-comp-DPTJTP-fit-young-man-standing-with-arms-crossed-in-fitness-studio-DPTJTP-jpg
Object Detection
29 images·1 model·11