Related Objects of Interest: ==============================, the following pre-processing was applied to each image:, * auto-orientation of pixel data (with exif-orientation stripping), the following augmentation was applied to create 3 versions of each source image:, * annotate, and create datasets, * collect & organize images, * export, train, and deploy computer vision models, * use active learning to improve your dataset over time, * collaborate with your team on computer vision projects, roboflow is an end-to-end computer vision platform that helps you
Top Rotation Datasets and Models
The datasets below can be used to train fine-tuned models for rotation detection. You can explore each dataset in your browser using Roboflow and export the dataset into one of many formats.
At the bottom of this page, we have guides on how to train a model using the rotation datasets below.
by learn
81 images 11 classes
3006 images 38 classes
* 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) 13 14 15 16 17 18 19 20 21 22 23 24 25 26
by bcvt
9760 images 33 classes
* 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 time 21 22 23 24 25 26 27 28 29 30
by Crickets
6558 images 52 classes
* 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 time 23 24 25 26 27 28 30 31 32 33
1720 images 26 classes
* 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 time 22 23 24 25 ============================== American Sign Language Letters - v1 v1
by University
662 images 4 classes
by project
1192 images 59 classes
Bus Stoprotation Bus only Lanerotation Chevron Markersrotation Children present- crossing Aheadrotation Compulsory Roundaboutrotation Compulsory Roundaboutrotation\ Cross Roads Aheadrotation Cycle Lane Aheadrotation Cycle only Lanerotation Directional Sign (EXPRESS WAY)rotation Directional Sign (NORMAL)rotation Directional Sign (NORMAL)rotation\ Double bend ahead End of Expresswayrotation Entrance to an Expresswayrotation Give Way Aheadrotation Give Way to vehicles on the adjacent roadrotation Height Limitrotation L Merges Aheadrotation Left Bend Ahead
by Car damage
9129 images 31 classes
* 50% probability of horizontal flip * 50% probability of vertical flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Random brigthness adjustment of between -25 and +25 percent * Random rotation of between -15 and +15 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 time 26 27 28 29 30 ============================== Car-damage are annotated in YOLOv8 format.
by Marco
9560 images 52 classes
* 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 time 29 30
by dhruti
1720 images 26 classes
* 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) 15 16 17 18 19 20 21 22 23 24 25 ==============================
by dev1
80 images 29 classes
* 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 time 20 21 22 23 24 25 26 27
by Test
4176 images 43 classes
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 time 20 21 22 23 24 25 26 27 28 29 30
by ingredients
9335 images 38 classes
* 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 time 23 24 25 26 27 28 29 30
by Amrutha
1720 images 26 classes
* 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 time 22 23 24 25 ============================== American Sign Language Letters - v1 v1
840 images 30 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 3 pixels * Random exposure adjustment of between -20 and +20 percent * Random rotation of between -3 and +3 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 time 23 24 25 26 27 28 29 ==============================
by Dretn
1193 images 80 classes
* 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 time 24 25 26 27 28 29 30
by tfg
8040 images 20 classes
* 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 time 18 19 ============================== For state of the art Computer Vision training notebooks you can use with this dataset, Grad_Project - v3 2023-03-16 3:09am Roboflow is an end-to-end computer vision platform that helps you The 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 GMT To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com Traffic-Signs are annotated in YOLOv8 format. visit https://github.com/roboflow/notebooks
by My
1720 images 26 classes
* 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) 15 16 17 18 19 20 21 22 23 24 25 ==============================
by Work
4514 images 194 classes
* 50% probability of horizontal flip * Auto-contrast via histogram equalization * 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 * Random brigthness adjustment of between -25 and +25 percent * Random exposure adjustment of between -16 and +16 percent * Random rotation of between -12 and +12 degrees * Random shear of between -12° to +12° horizontally and -10° to +10° vertically * Randomly crop between 0 and 20 percent of the bounding box * 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 time 100 101 102
9949 images 39 classes
* 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 time 23 24 25 26 27 28 29 30
by Test ES
1493 images 37 classes
* Random rotation of between -15 and +15 degrees * Random shear of between -15° to +15° horizontally and -15° to +15° vertically * 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 time 21 22 23 24 25 26 27 28 29 30 31