Top Probability Datasets and Models
The datasets below can be used to train fine-tuned models for probability detection. You can explore each dataset in your browser using Roboflow and export the dataset into one of many formats.
Object Detection
7.22k images·1 model·10
* 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
Object Detection
4.66k images·2 models
hint-guidehint-reddotnavigation-DailyDiscountnavigation-FirstChargenavigation-FirstChargegift1navigation-FirstChargegift2navigation-FirstChargegift3navigation-Warordernavigation-bagnavigation-bag-offnavigation-bountyMissionnavigation-build-lingze-offnavigation-build-shanhai-offnavigation-communitynavigation-dailySumnavigation-dailyTasknavigation-dianjiangtainavigation-fenghuotai-offnavigation-fightnavigation-friends
Object Detection
1.61k images·1 model·3
object* 50% probability of horizontal flip* 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 time==============================Animal detect - v2 2023-04-25 3:38pmAnimals are annotated in YOLOv8 format.For state of the art Computer Vision training notebooks you can use with this dataset,Person dataset - v3 2023-11-16 1:52amRoboflow is an end-to-end computer vision platform that helps youThe dataset includes 269 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 April 25, 2023 at 10:09 AM GMTTo find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
Object Detection
2.45k images·1 model
object* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* 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 time20==============================For state of the art Computer Vision training notebooks you can use with this dataset,Roboflow is an end-to-end computer vision platform that helps youSignals are annotated in YOLO v5 PyTorch format.The dataset includes 2587 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 November 25, 2023 at 12:38 AM GMTTo find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
Object Detection
1.43k images·1 model
-- 50- probability of horizontal flip- Auto-orientation of pixel data -with EXIF-orientation stripping-- Grayscale -CRT phosphor-- Random Gaussian blur of between 0 and 2-5 pixels- Randomly crop between 0 and 20 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 time2324262728293031
Classification
9.56k images·2
* 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 1.75 pixels* Random brigthness adjustment of between -25 and +25 percent* Random exposure adjustment of between -15 and +15 percent* Random rotation of between -10 and +10 degrees* Random shear of between -2° to +2° horizontally and -2° to +2° vertically* Randomly crop between 0 and 15 percent of the image* Resize to 640x640 (Stretch)* Salt and pepper noise was applied to 2 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 time2930
Object Detection
3.26k images·2
* Auto-contrast via contrast stretching* 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 shear of between -14° to +14° horizontally and -15° to +15° vertically* Resize to 800x800 (Stretch)* Salt and pepper noise was applied to 1.13 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 time100101102103104105106107
Object Detection
1.76k images
* 50% probability of horizontal flip* 50% probability of vertical flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random Gaussian blur of between 0 and 1.5 pixels* Random exposure adjustment of between -10 and +10 percent* 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 time242526==============================American Sign Language Letters - v2 2024-12-10 12:43pmFor state of the art Computer Vision training notebooks you can use with this dataset,Letters are annotated in YOLOv8 format.
Classification
1.72k images·3
* 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
Object Detection
1.73k images·1 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
Object Detection
50 images·8
* 50% probability of horizontal flip* 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* use active learning to improve your dataset over time==============================Animal detect - v2 2023-04-25 3:38pmAnimals are annotated in YOLOv8 format.For state of the art Computer Vision training notebooks you can use with this dataset,Roboflow is an end-to-end computer vision platform that helps youThe dataset includes 269 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 April 25, 2023 at 10:09 AM GMTTo find over 100k other datasets and pre-trained models, visit https://universe.roboflow.comvisit https://github.com/roboflow/notebooks
Object Detection
244 images·1 model·5
* 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))-black chocolate-brown chocolate-gift chocolate-wave chocolate-white chocolate15161718192021
Object Detection
69 images·1 model
Object Detection
1.19k images·1 model·1
* 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
Object Detection
1.34k images
-- 50- probability of horizontal flip- 50- probability of vertical flip- Auto-orientation of pixel data -with EXIF-orientation stripping-- Grayscale -CRT phosphor-- Random Gaussian blur of between 0 and 0-8 pixels- Random rotation of between -12 and -12 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 time100101102103104105
Classification
2.24k images·1 model·2
freshobject* 50% probability of horizontal flip* 50% probability of vertical flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Resize to 640x640 (Stretch)* annotate, and create datasets* annotate, and create datasets * collect & organize images* annotate, and create datasets * understand and search unstructured image data* 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 time21222323 242425
Object Detection
245 images·1 model·2
chocolate* 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.It includes 267 images.The following augmentation was applied to create 5 versions of each source image:
Object Detection
72 images·38
* 50% probability of horizontal flip* 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 time==============================Animal detect - v2 2023-04-25 3:38pmAnimals are annotated in YOLOv8 format.For state of the art Computer Vision training notebooks you can use with this dataset,Roboflow is an end-to-end computer vision platform that helps youThe dataset includes 269 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 April 25, 2023 at 10:09 AM GMTTo find over 100k other datasets and pre-trained models, visit https://universe.roboflow.comvisit https://github.com/roboflow/notebooks
Instance Segmentation
101 images·2 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
Instance Segmentation
4.86k images·2 models·3
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
Object Detection
5.2k images
* 50% probability of horizontal flip* 50% probability of vertical flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random Gaussian blur of between 0 and 4 pixels* Resize to 640x640 (Fit (reflect edges))* 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 time222324==============================For state of the art Computer Vision training notebooks you can use with this dataset,Plant are annotated in YOLOv8 format.Plants Detection - v13 2023-07-28 5:57pmRoboflow is an end-to-end computer vision platform that helps youThe dataset includes 6045 images.
Object Detection
103 images·1 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
Object Detection
1.72k images·1 model·1
* 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==============================
Object Detection
10.4k images·1 model
meat* 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.22232425262728
Object Detection
595 images
roundaboutslippery roadtraffic signal* 50% probability of vertical flip* 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 data100120293039405058606366
Object Detection
8.02k images·1 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 time------------------------------10012021222324252627
Object Detection
80 images·1 model·1
* 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
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
8.49k images·2
* 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* Grayscale (CRT phosphor)* 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 and search unstructured image data* use active learning to improve your dataset over time222324252627282930
Object Detection
3.01k images·1 model·5
* 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
Object Detection
246 images·1 model·1
* 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
Object Detection
1.08k images·3
* 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 shear of between -10° to +10° horizontally and -10° to +10° 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 time21222324252627282930
Object Detection
470 images
* 50% probability of horizontal flip* Auto-contrast via histogram equalization* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random Gaussian blur of between 0 and 2 pixels* Random brigthness adjustment of between -50 and 0 percent* Random rotation of between -25 and +25 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 time==============================Deer-on-roads are annotated in YOLOv8 format.For state of the art Computer Vision training notebooks you can use with this dataset,Roboflow is an end-to-end computer vision platform that helps youThe dataset includes 734 images.The following augmentation was applied to create 3 versions of each source image:
Object Detection
2.82k images
* 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 0.75 pixels* Random brigthness adjustment of between -26 and +26 percent* Resize to 640x640 (Stretch)* Salt and pepper noise was applied to 3 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 time23242526==============================Characters are annotated in YOLOv8 format.For state of the art Computer Vision training notebooks you can use with this dataset,Roboflow is an end-to-end computer vision platform that helps you
Object Detection
520 images
* 50% probability of horizontal flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* 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 time202122232425==============================For state of the art Computer Vision training notebooks you can use with this dataset,Roboflow is an end-to-end computer vision platform that helps youSSL Detection - v5 ssl-detectionSibilang are annotated in YOLOv8 format.
Object Detection
3.33k images·3
* 50% probability of horizontal flip* 50% probability of vertical flip* Auto-orientation of pixel data (with EXIF-orientation stripping)* Random rotation of between -15 and +15 degrees* Random shear of between -4° to +4° horizontally and -4° to +4° vertically* Resize to 420x420 (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 time==============================Alternaria-SpotBrassicaceae Diseases - v1 2024-11-26 12:42pmBrassicaceae Diseases - v2 2024-11-26 12:49pmBrassicaceae Diseases - v3 2024-11-26 12:56pmClub RootDiseases-wbZv-T77K are annotated in YOLOv8 format.Downy-Mildew
Object Detection
1.33k images
* Auto-orientation of pixel data (with EXIF-orientation stripping)* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise* 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 time202122232425==============================BrahmaraChaturaFor state of the art Computer Vision training notebooks you can use with this dataset,Hamsapaksha
Object Detection
200 images·1 model·1
* 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* Resize to 600x600 (Fit (white edges))* 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 time==============================Chocolates are annotated in YOLOv8 format.For state of the art Computer Vision training notebooks you can use with this dataset,Roboflow is an end-to-end computer vision platform that helps youThe dataset includes 267 images.The following augmentation was applied to create 5 versions of each source image:The following pre-processing was applied to each image:This dataset was exported via roboflow.com on June 4, 2023 at 12:27 PM GMTTo find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
Object Detection
200 images·1 model·1
* 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* Resize to 600x600 (Fit (white edges))* 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 time==============================ChocolatesChocolates are annotated in YOLOv8 format.For state of the art Computer Vision training notebooks you can use with this dataset,Roboflow is an end-to-end computer vision platform that helps youThe dataset includes 267 images.The following augmentation was applied to create 5 versions of each source image:The following pre-processing was applied to each image:This dataset was exported via roboflow.com on June 4, 2023 at 12:27 PM GMT
Classification
2.86k images
- Auto-orientation of pixel data (with EXIF-orientation stripping)- Auto-orientation of pixel data (with EXIF-orientation stripping) - Random rotation of between -21 and +21 degrees - Resize to 600x600 (Stretch) - annotate, and create datasets - collaborate with your team on computer vision projects - collect & organize images - understand and search unstructured image data 23 26 32 35 36 ============================== MedicineBoxes - v1 2023-11-16 10:14am The dataset includes 2859 images- The following pre-processing was applied to each image:- Auto-orientation of pixel data (with EXIF-orientation stripping) - Random rotation of between -21 and +21 degrees - Resize to 600x600 (Stretch) - collect & organize images - understand and search unstructured image data 23 26 32 33 35 36 ============================== MedicineBoxes - v1 2023-11-16 10:14am The dataset includes 2859 images- The following pre-processing was applied to each image:- Auto-orientation of pixel data (with EXIF-orientation stripping) - Random rotation of between -21 and +21 degrees - collaborate with your team on computer vision projects - collect & organize images 23 32 35 The dataset includes 2859 images- The following pre-processing was applied to each image:- Auto-orientation of pixel data (with EXIF-orientation stripping) - Random rotation of between -21 and +21 degrees - collect & organize images 23 32 35 For state of the art Computer Vision training notebooks you can use with this dataset, The dataset includes 2859 images-- Auto-orientation of pixel data (with EXIF-orientation stripping) - Random rotation of between -21 and +21 degrees - collect & organize images 32- Auto-orientation of pixel data (with EXIF-orientation stripping) - Random rotation of between -21 and +21 degrees 23 32 35 The dataset includes 2859 images-- Auto-orientation of pixel data (with EXIF-orientation stripping) - Resize to 600x600 (Stretch) - annotate, and create datasets - collaborate with your team on computer vision projects - collect & organize images - understand and search unstructured image data 26 32 35 36 ============================== MedicineBoxes - v1 2023-11-16 10:14am The following pre-processing was applied to each image:- Auto-orientation of pixel data (with EXIF-orientation stripping) - Resize to 600x600 (Stretch) - collaborate with your team on computer vision projects - collect & organize images - understand and search unstructured image data 26 32 35 36 ============================== MedicineBoxes - v1 2023-11-16 10:14am The following pre-processing was applied to each image:- Auto-orientation of pixel data (with EXIF-orientation stripping) - Resize to 600x600 (Stretch) - collect & organize images 32 For state of the art Computer Vision training notebooks you can use with this dataset,- Auto-orientation of pixel data (with EXIF-orientation stripping) - collaborate with your team on computer vision projects - collect & organize images 32- Auto-orientation of pixel data (with EXIF-orientation stripping) - collect & organize images 23 32 35 For state of the art Computer Vision training notebooks you can use with this dataset, The dataset includes 2859 images-- Auto-orientation of pixel data (with EXIF-orientation stripping) - collect & organize images 32- Auto-orientation of pixel data (with EXIF-orientation stripping) - collect & organize images 32 For state of the art Computer Vision training notebooks you can use with this dataset,- Auto-orientation of pixel data (with EXIF-orientation stripping) - collect & organize images 32 The following pre-processing was applied to each image:- Auto-orientation of pixel data (with EXIF-orientation stripping) 23- Auto-orientation of pixel data (with EXIF-orientation stripping) 35 The following pre-processing was applied to each image:- Auto-orientation of pixel data (with EXIF-orientation stripping) For state of the art Computer Vision training notebooks you can use with this dataset,- Auto-orientation of pixel data (with EXIF-orientation stripping) The following pre-processing was applied to each image:- Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
Object Detection
9.34k images·3 models·2
* 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
Instance Segmentation
692 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* 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
Instance Segmentation
1.73k images·1 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
Object Detection
1.72k images
* 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==============================
Object Detection
1.53k images·1 model
carmotorcyclesmartphonetruck* 50% probability of horizontal flip* 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 time==============================Animal detect - v2 2023-04-25 3:38pmAnimals are annotated in YOLOv8 format.FaceFor state of the art Computer Vision training notebooks you can use with this dataset,Roboflow is an end-to-end computer vision platform that helps youThe dataset includes 269 images.The following augmentation was applied to create 3 versions of each source image:The following pre-processing was applied to each image:
Object Detection
6.56k images·1 model·1
* 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