Related Objects of Interest: ==============================, the following pre-processing was applied to each image:, * annotate, and create datasets, * collect & organize images, * export, train, and deploy computer vision models, * use active learning to improve your dataset over time, the following augmentation was applied to create 3 versions of each source image:, * collaborate with your team on computer vision projects, * auto-orientation of pixel data (with exif-orientation stripping), * understand and search unstructured image data
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 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 Naer
8478 images 53 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Grayscale (CRT phosphor) * Random brigthness adjustment of between -25 and +25 percent * Random exposure adjustment of between -25 and +25 percent * 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 24 25 26 27 28 29 30 31
259 images 11 classes
by kaushik
8015 images 78 classes
* 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 100 120 21 22 23 24 25 26 27 28
by college
3480 images 27 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 2.5 pixels * Random brigthness adjustment of between -25 and +25 percent * Resize to 640x640 (Stretch) * Salt and pepper noise was applied to 14 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 ============================== For state of the art Computer Vision training notebooks you can use with this dataset, Road Sign Detector - v7 Add blur and noise Roboflow is an end-to-end computer vision platform that helps you
by college
3480 images 27 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 2.5 pixels * Random brigthness adjustment of between -25 and +25 percent * Resize to 640x640 (Stretch) * Salt and pepper noise was applied to 14 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 ============================== For state of the art Computer Vision training notebooks you can use with this dataset, Road Sign Detector - v7 Add blur and noise Roboflow is an end-to-end computer vision platform that helps you
by UM
1027 images 14 classes
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 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
1760 images 38 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random shear of between -15° to +15° horizontally and -15° to +15° vertically * Resize to 416x416 (Stretch) * Salt and pepper noise was applied to 4 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 Test
995 images 10 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 melon
7221 images 30 classes
* 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 time 22 23 24 26 27 29 ============================== Axe Bazooka Gun Katana
by Thomas
2086 images 11 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 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
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 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 Naer
5759 images 40 classes
* Random brigthness 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 * understand and search unstructured image data 21 22 23 A B C D E F G H I Isyarat - v2 2023-05-09 12:25am J
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 pr1
2400 images 25 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random brigthness adjustment of between -15 and +15 percent * Resize to 320x320 (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 ============================== For state of the art Computer Vision training notebooks you can use with this dataset, IDEL - v2 2022-11-04 5:31pm Roboflow is an end-to-end computer vision platform that helps you The dataset includes 2432 images. The following augmentation was applied to create 3 versions of each source image:
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