Related Objects of Interest: ==============================, * collect & organize images, 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:, * 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 time
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 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
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 aditya
50 images 8 classes
by UIU
7810 images 10 classes
106 images 3398 classes
by pothole
7814 images 10 classes
by Deneme
3256 images 692 classes
* 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 time 100 101 102 103 104 105 106 107
by Deneme
3362 images 694 classes
* 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 time 100 101 102 103 104 105 106 107
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) * 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
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 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 UQO
5995 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 HADJEM
1238 images 103 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random brigthness adjustment of between -1 and +1 percent * Random rotation of between -1 and +1 degrees * Random shear of between -1° to +1° horizontally and -0° to +0° 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 22 23 24 25 26 27 28 29 30
93 images 7 classes
by pothole
1130 images 6 classes
by MY
9880 images 60 classes
* 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 time 20 21 22 23 24 25 26 27 28 29 30
by dsad
7635 images 86 classes
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 time 28 29 30
by KKJJ8899
760 images 79 classes
* 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 time 20 21 22 23 24 25 26 27 28 29 30
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
7036 images 6 classes
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 ==============================
50 images 6 classes