Related Objects of Interest: ==============================, * auto-orientation of pixel data (with exif-orientation stripping), the following augmentation was applied to create 3 versions of each source image:, the following pre-processing was applied to each image:, * collect & organize images, * annotate, and create datasets, * export, train, and deploy computer vision models, * use active learning to improve your dataset over time, for state of the art computer vision training notebooks you can use with this dataset,, * 50% probability of horizontal flip
Top Random Datasets and Models
The datasets below can be used to train fine-tuned models for random 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 random datasets below.
by pothole
7814 images 10 classes
by MySpace
3756 images 15 classes
Centre Line- Alligator Centre Line- Single and Multiple Flushing Long Meander and Midlane Longitudinal Wheel Track- Single and Multiple Longitudinal Wheel Track-Alligator Pavement Edge- Single and Multiple Pavement Edge-Alligator Pothole Random-Map Ravelling and C- Agg- Loss Rippling and Shoving Transverse- Alligator Transverse- Half- Full and Multiple Wheel Track Rutting
by MySpace
3647 images 15 classes
Centre Line- Alligator Centre Line- Single and Multiple Flushing Long Meander and Midlane Longitudinal Wheel Track- Single and Multiple Longitudinal Wheel Track-Alligator Pavement Edge- Single and Multiple Pavement Edge-Alligator Pothole Random-Map Ravelling and C- Agg- Loss Rippling and Shoving Transverse- Alligator Transverse- Half- Full and Multiple Wheel Track Rutting
by recipeVision
1995 images 47 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random rotation of between -15 and +15 degrees * Random rotation of between -37 and +37 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 22 23 24 25 26 27 28 29 30 31
by UM
1027 images 14 classes
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 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
50 images 6 classes
by AI Class
154 images 5 classes
93 images 7 classes
by router
1647 images 8 classes
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 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.
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 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 Gaurang
6040 images 12 classes
* Random shear of between -10° to +10° horizontally and -10° to +10° vertically * collect & organize images 100 200 2000 50 500 ============================== Coin For state of the art Computer Vision training notebooks you can use with this dataset, Rupee This dataset was exported via roboflow.com on January 30, 2024 at 2:51 PM GMT