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 James Mixon
5399 images 73 classes
* 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 time 0 1 10 100 1000 10th Spot 11 12 13
106 images 3398 classes
368 images 6 classes
7036 images 6 classes
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
107 images 6 classes
402 images 6 classes
181 images 6 classes
259 images 11 classes
by Test
1265 images 13 classes
75 images 6 classes
by Test
878 images 11 classes
by go4av05
8659 images 40 classes
* 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 time 17 18 19 20 21 22 23 24 25 26
by Test
995 images 10 classes
9687 images 22 classes
by ThreatGuard
673 images 6 classes
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 Thomas
2086 images 11 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 UM
1027 images 14 classes