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Related Objects of Interest: ============================== , the following pre-processing was applied to each image: , * annotate, and create datasets , * auto-orientation of pixel data (with exif-orientation stripping) , * export, train, and deploy computer vision models , * use active learning to improve your dataset over time , the following augmentation was applied to create 3 versions of each source image: , * collaborate with your team on computer vision projects , * collect & organize images , roboflow is an end-to-end computer vision platform that helps you
Top Between Computer Vision Models
The models below have been fine-tuned for various between detection tasks. You can try out each model in your browser, or test an edge deployment solution (i.e. to an NVIDIA Jetson). You can use the datasets associated with the models below as a starting point for building your own between detection model.
At the bottom of this page, we have guides on how to count betweens in images and videos.
7221 images1 model
* 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 Gaussian blur of between 0 and 2.5 pixels* Resize to 640x640 (Stretch)* annotate, and create datasets* collaborate with your team on computer vision projects* export, train, and deploy computer vision models* understand and search unstructured image data* use active learning to improve your dataset over time222324262729==============================AxeBazookaGunKatana
391 images1 model
(A-4) Simple gap between concrete units with no movement of the wall(E-1)One concrete unit extracted-(E-2)More than one concrete unit extracted-(F-1)One concrete unit out of layer-(F-2)More than one concrete unit out of layer-(G-1)Concrete units need to be rearranged-ArrangementG-1Gap A-3Gap A-4More than one cracked concrete unitOne concrete unit out of layer-cracked D-3disintegrated D-6
3006 images1 model
* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise* Random rotation of between -15 and +15 degrees* Randomly crop between 0 and 20 percent of the image* 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 time2324252627282930
8015 images1 model
* 50% probability of horizontal flip* 50% probability of vertical flip* Random Gaussian blur of between 0 and 3.25 pixels* Randomly crop between 0 and 33 percent of the image* 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 time1001202122232425262728
3480 images1 model
* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random Gaussian blur of between 0 and 2.5 pixels* Random brigthness adjustment of between -25 and +25 percent* Resize to 640x640 (Stretch)* Salt and pepper noise was applied to 14 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 time2223242526==============================For state of the art Computer Vision training notebooks you can use with this dataset,Road Sign Detector - v7 Add blur and noiseRoboflow is an end-to-end computer vision platform that helps you
3006 images1 model
* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise* Random rotation of between -15 and +15 degrees* Randomly crop between 0 and 20 percent of the image* Resize to 416x416 (Stretch)1314151617181920212223242526
9760 images2 models
* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random Gaussian blur of between 0 and 1.5 pixels* Random rotation of between -15 and +15 degrees* 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 time21222324252627282930
134 images1 model
ATMATM openedAttackerHooded user (male)Inserting cardMight have an object (hidden hand)PersonPerson assisting (spurious activity)Person bendingPhonePhysical AssaultPhysical Assault (male)Reaching behind ATMSafe Attempted to be openedSecurity (male)Setting up Phone (record pin)Stealing customer details (peeking)Threat (fight)USBUn-identifiable
706 images1 model
animalcardrinkmushroomrockwine glass2 bottles on a rock next to fruit in front of orange background.2 brown paper boxes in front of light brown background2 cream bottles in front of blue tiles2 macarons on a rock block in front of light orange background3 black bottles next to yellow stairs and dry flower with shadow3 blue bottles on blue plates next to dry nuts3 bottles in front of light background with shadows3 bottles in front of light brown background with shadows.3 bottles in front of light gray background with shadows of a plant.3 boxes in front of dark background3 perfume bottles on reflective gray surface4 paper boxes in front of light yellow background4 potteries on a marble table6 white bowls of spices on a wooden surface
4176 images1 model
object* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random rotation of between -45 and +45 degrees* Resize to 640x640 (Stretch)* annotate, and create datasets* collect & organize images* export, train, and deploy computer vision models* understand and search unstructured image data* use active learning to improve your dataset over time2021222324252627282930
704 images1 model
Risk point：Several vehicles are parked in non-parking areas.Risk point：The presence of motorcycles and bicycles within the same lanes as cars.Risk-point:There-are-pedestrians-walking-and-crossing-the-road-in-between-cars.Risk-point：Some-vehicles-appear-to-be-driving-in-the-wrong-direction.Risk-point：The-high-density-of-vehicles-can-lead-to-congestion.
4856 images1 model
chickentrain#-Healthy-and-Sick-Chicken-Detection->-2023-02-04-2:29pm*-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*-Random-exposure-adjustment-of-between--25-and-+25-percent*-Resize-to-416x416-(Stretch)*-annotate*-collaborate-with-your-team-on-computer-vision-projects*-collect-&-organize-images*-export*-understand-and-search-unstructured-image-data*-use-active-learning-to-improve-your-dataset-over-time1WOC-are-annotated-in-Tensorflow-Object-Detection-format.2024-at-10:31-AM-GMT==============================For-state-of-the-art-Computer-Vision-training-notebooks-you-can-use-with-this-datasetHealthy-and-Sick-Chicken-Detection---v18-2023-02-04-2:29pm
5399 images1 model
* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random rotation of between -5 and +5 degrees* Randomly crop between 0 and 30 percent of the image* Resize to 640x352 (Fit within)* Salt and pepper noise was applied to 0 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 time0110100100010th Spot111213
246 images1 model
* 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 Gaussian blur of between 0 and 0.5 pixels* Random brigthness adjustment of between -16 and +16 percent* Random exposure adjustment of between -6 and +6 percent* Resize to 600x600 (Fit (white edges))15161718192021==============================Chocolates are annotated in YOLO v5 PyTorch format.Dark MarzipanIt includes 267 images.Milk California Brittle
1720 images2 models
* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random Gaussian blur of between 0 and 1.25 pixels* Random brigthness adjustment of between -25 and +25 percent* Random rotation of between -5 and +5 degrees* Random shear of between -5° to +5° horizontally and -5° to +5° vertically* Randomly crop between 0 and 20 percent of the image* 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 unstructured image data* use active learning to improve your dataset over time22232425==============================American Sign Language Letters - v1 v1
8659 images1 model
* Random Gaussian blur of between 0 and 2.5 pixels* Random brigthness adjustment of between -25 and +25 percent* Random exposure adjustment of between -25 and +25 percent* 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 time17181920212223242526
9880 images1 model
* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random rotation of between -20 and +20 degrees* 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 time2021222324252627282930
9949 images1 model
* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random Gaussian blur of between 0 and 5 pixels* Random exposure adjustment of between -20 and +20 percent* Random rotation of between -10 and +10 degrees* Random shear of between -15° to +15° horizontally and -15° to +15° vertically* Resize to 640x640 (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 time2324252627282930
1728 images1 model
* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random brigthness adjustment of between -25 and +25 percent* Random rotation of between -5 and +5 degrees* Random shear of between -5° to +5° horizontally and -5° to +5° vertically* Randomly crop between 0 and 20 percent of the image* 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 unstructured image data* use active learning to improve your dataset over time2324==============================AASLAmerican Sign Language Letters - v1 v1B
1720 images1 model
* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random Gaussian blur of between 0 and 1.25 pixels* Random brigthness adjustment of between -25 and +25 percent* Random rotation of between -5 and +5 degrees* Random shear of between -5° to +5° horizontally and -5° to +5° vertically* Randomly crop between 0 and 20 percent of the image* Resize to 416x416 (Stretch)1516171819202122232425==============================
9335 images3 models
* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise* Random rotation of between -15 and +15 degrees* Randomly crop between 0 and 20 percent of the image* 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 time2324252627282930
7635 images1 model
object* 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* Random Gaussian blur of between 0 and 1.5 pixels* Random brigthness adjustment of between -25 and +25 percent* Random brigthness adjustment of between -30 and +30 percent* Random rotation of between -23 and +23 degrees* 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 time282930
1193 images1 model
* 50% probability of horizontal flip* 50% probability of vertical flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random exposure adjustment of between -15 and +15 percent* Random rotation of between -15 and +15 degrees* Random shear of between -15° to +15° horizontally and -15° to +15° vertically* Resize to 640x640 (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 time24252627282930
1728 images1 model
* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random brigthness adjustment of between -25 and +25 percent* Random rotation of between -5 and +5 degrees* Random shear of between -5° to +5° horizontally and -5° to +5° vertically* Randomly crop between 0 and 20 percent of the image* 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 unstructured image data* use active learning to improve your dataset over time2324==============================AASLAmerican Sign Language Letters - v1 v1B
6558 images1 model
* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random rotation of between -15 and +15 degrees* Resize to 640x640 (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 time23242526272830313233
23007 images1 model
- Auto-contrast via contrast stretching- Random brigthness adjustment of between -30 and +30 percent- Random rotation of between -15 and +15 degrees- Random shear of between -10° to +10° horizontally and -10° to +10° vertically- collaborate with your team on computer vision projects- understand and search unstructured image data- use active learning to improve your dataset over time21222324252627282930313233
1430 images1 model
80 images1 model
* 50% probability of horizontal 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 -15 and +15 degrees* Resize to 416x416 (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 unstructured image data* use active learning to improve your dataset over time2021222324252627
760 images1 model
* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random brigthness adjustment of between -3 and +3 percent* Resize to 640x640 (Fit within)* 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 time2021222324252627282930
101 images2 models
* 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* Equal probability of one of the following 90-degree rotations: none, clockwise, upside-down* Random Gaussian blur of between 0 and 1.6 pixels* Random Gaussian blur of between 0 and 10.2 pixels* Random brigthness adjustment of between -40 and +40 percent* Random brigthness adjustment of between -53 and +53 percent* Random exposure adjustment of between -27 and +27 percent* Random exposure adjustment of between -41 and +41 percent* Random rotation of between -35 and +35 degrees* Random rotation of between -45 and +45 degrees* Random shear of between -16° to +16° horizontally and -22° to +22° vertically* Random shear of between -37° to +37° horizontally and -29° to +29° vertically* Randomly crop between 22 and 61 percent of the bounding box* Randomly crop between 5 and 18 percent of the image* Resize to 640x640 (Stretch)* Salt and pepper noise was applied to 0.81 percent of pixels* Salt and pepper noise was applied to 4.62 percent of pixels
103 images1 model
motor* 50% probability of horizontal flip* 50% probability of vertical flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random rotation of between -20 and +20 degrees* Resize to 600x400 (Fit within)==============================Kendaraan - v2 Kendaraan v2The following augmentation was applied to create 3 versions of each source image:The following pre-processing was applied to each image:This dataset was exported via roboflowangkutan kotaangkutan kotabajajbecakcolorkapalkeretamobilperahu
8040 images1 model
* Random rotation of between -15 and +15 degrees* 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 time1819==============================For state of the art Computer Vision training notebooks you can use with this dataset,Grad_Project - v3 2023-03-16 3:09amRoboflow is an end-to-end computer vision platform that helps youThe dataset includes 8077 images.The following augmentation was applied to create 3 versions of each source image:The following pre-processing was applied to each image:This dataset was exported via roboflow.com on March 16, 2023 at 12:52 PM GMTTo find over 100k other datasets and pre-trained models, visit https://universe.roboflow.comTraffic-Signs are annotated in YOLOv8 format.visit https://github.com/roboflow/notebooks
8205 images1 model
* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random Gaussian blur of between 0 and 5 pixels* Random shear of between -15° to +15° horizontally and -15° to +15° vertically* Resize to 640x640 (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 time1232527282930313334
10447 images1 model
* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random rotation of between -15 and +15 degrees* Randomly crop between 0 and 20 percent of the image* Resize to 640x640 (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 time1-6lQ6-ini4 are annotated in YOLOv11 format.2223242526272829