Related Objects of Interest: * auto-orientation of pixel data (with exif-orientation stripping), ==============================, the following pre-processing was applied to each image:, * collaborate with your team on computer vision projects, * export, train, and deploy computer vision models, * understand and search unstructured image data, visit https://github.com/roboflow/notebooks, * annotate, and create datasets, * collect & organize images, * use active learning to improve your dataset over time
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
1158 images 57 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * 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 1 10(10pin) 10(4pin) 11 13 14 1400 15 16 17 18 19
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
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 ttest
8487 images 54 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 * Grayscale (CRT phosphor) * 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 and search unstructured image data * use active learning to improve your dataset over time 22 23 24 25 26 27 28 29 30
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
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 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
356 images 53 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * 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 19 20 21 22 23 24 25 26 27 28 29 30
by Fun stuff
4793 images 93 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 * 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
by train image
5360 images 17 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * 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 ============================== Food_detection - v1 Food_recognition Foods are annotated in YOLOv8 format. For state of the art Computer Vision training notebooks you can use with this dataset, Roboflow is an end-to-end computer vision platform that helps you The dataset includes 5411 images. The following pre-processing was applied to each image: This dataset was exported via roboflow.com on July 3, 2023 at 2:21 PM GMT To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com visit https://github.com/roboflow/notebooks
1994 images 37 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 * Resize to 180x300 (Stretch) * Salt and pepper noise was applied to 1 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
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 My workspace
560 images 59 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * 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 11 12 13 14 15 16 17 18 19 20 21 22 23
by Laxav
1800 images 27 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * 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 0 13 16 18 21 25 3 5 ============================== A American Sign Language Letters - v1 v1 For state of the art Computer Vision training notebooks you can use with this dataset,
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 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
1440 images 35 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * 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 19 20 21 22 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) * 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 Thavcayena
5122 images 41 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Grayscale (CRT phosphor) * 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 0 1 2 3 4 5 6 7 8 9 ==============================
by grad project
523 images 40 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch) * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data 1 19 2 20 21 23 3 4 5 6 7 8 9 ==============================
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
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 KUST
1203 images 27 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Grayscale (CRT phosphor) * Resize to 640x640 (Fit (white edges)) * 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 2 5 6 7 8 ============================== For state of the art Computer Vision training notebooks you can use with this dataset, LCD - v14 2023-10-13 2:03pm LCD-Display are annotated in YOLOv8 format.
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 KARY
1270 images 51 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * 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 19 20 21 22 23 24 25 26 27 28 29 30
by kary
400 images 40 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * 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 19 ============================== For state of the art Computer Vision training notebooks you can use with this dataset, IRIS RECONIGTION PFE_essai - v1 IRISDATA_COMPL IRIS-83ep are annotated in YOLOv8 format. IRIS52 IRIS53 IRIS54 IRIS55 IRIS56 IRIS57 IRIS59
1039 images 49 classes
- - Auto-orientation of pixel data -with EXIF-orientation stripping- - 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 10-10pin- 11 14 1400 15 16 17 18 19 20 21
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