Top 1880 Datasets and Models
The datasets below can be used to train fine-tuned models for 1880 detection. You can explore each dataset in your browser using Roboflow and export the dataset into one of many formats.
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
800 images
* 50% probability of horizontal flip* 50% probability of vertical flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down* Random rotation of between -11 and +11 degrees* Random shear of between -15° to +15° horizontally and -15° to +15° vertically* Resize to 640x640 (Stretch)* Salt and pepper noise was applied to 5 percent of pixels* annotate, and create datasets* collaborate with your team on computer vision projects* collect & organize images* export, train, and deploy computer vision models* understand and search unstructured image data* use active learning to improve your dataset over time10010001001100210031004
Object Detection
1.96k images
* Auto-orientation of pixel data (with EXIF-orientation stripping)* Resize to 416x416 (Stretch)* annotate, and create datasets* collaborate with your team on computer vision projects* collect & organize images* export, train, and deploy computer vision models* understand and search unstructured image data* use active learning to improve your dataset over time1001000100110021003100410051006100710081009101
Object Detection
3.02k images·1 model
Object Detection
529 images·1 model·2
* Auto-orientation of pixel data (with EXIF-orientation stripping)* annotate, and create datasets* collaborate with your team on computer vision projects* collect & organize images* export, train, and deploy computer vision models* understand and search unstructured image data* use active learning to improve your dataset over time10010001001100210031004100510061007100810091011010
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