Related Objects of Interest: ==============================, the following pre-processing was applied to each image:, * auto-orientation of pixel data (with exif-orientation stripping), * collect & organize images, * export, train, and deploy computer vision models, * annotate, and create datasets, * collaborate with your team on computer vision projects, * use active learning to improve your dataset over time, roboflow is an end-to-end computer vision platform that helps you, * understand and search unstructured image data
Top Resize Datasets and Models
The datasets below can be used to train fine-tuned models for resize 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 resize datasets below.
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
by AI
2240 images 34 classes
object * 50% probability of horizontal flip * 50% probability of vertical flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch) * annotate, and create datasets * annotate, and create datasets * collect & organize images * annotate, and create datasets * understand and search unstructured image data * 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 23 24 24 25 26
600 images 48 classes
elephant fox monkey pig tiger * 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
by 21Vision
40 images 52 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 sr
840 images 8 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 896x896 (Stretch) ============================== Fruits and Thumb detection - v5 v5 for yolov4 darknet Fruits-and-Thumb are annotated in YOLO v5 PyTorch format. It includes 859 images. The following pre-processing was applied to each image: This dataset was exported via roboflow.ai on April 15, 2022 at 7:08 AM GMT
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 testing
680 images 8 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch) ============================== Fruits and Thumb detection - v1 yolov5_v1 Fruits-and-Thumb are annotated in YOLO v5 PyTorch format. It includes 687 images. The following pre-processing was applied to each image: This dataset was exported via roboflow.ai on March 4, 2022 at 12:38 PM GMT
by Traffic
1958 images 1876 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 100 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 101
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.
880 images 11 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 416x416 (Stretch) 10 9 ============================== Ingredientsdataset are annotated in YOLO v5 PyTorch format. It includes 905 images. No image augmentation techniques were applied. The following pre-processing was applied to each image: This dataset was exported via roboflow.ai on May 30, 2021 at 10:39 AM GMT ingredients_dataset - v4 ingredients_data_meet_add
by SREEVISHAK V
888 images 5 classes
by ANPR
4000 images 32 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 k66a4
2914 images 59 classes
* Auto-contrast via contrast stretching * 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) 13 14 15 16 17 18 19 20 21 22 23 24 25 26
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 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 Flash0007
4181 images 55 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 test
1672 images 39 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
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
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 Mon
200 images 27 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 ============================== All_Connectors - v1 2024-04-02 3:22pm Connectors-LX2z are annotated in YOLOv8 format. For state of the art Computer Vision training notebooks you can use with this dataset,
280 images 26 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 ============================== All_Connectors - v2 2024-04-12 3:34pm Connectors-LX2z are annotated in YOLOv8 format. For state of the art Computer Vision training notebooks you can use with this dataset, No image augmentation techniques were applied.
1052 images 24 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 ============================== All_connections 20240402 draft 2 - v1 2024-04-12 2:43pm Connectors-eOTd-wazh are annotated in YOLOv8 format. For state of the art Computer Vision training notebooks you can use with this dataset, No image augmentation techniques were applied. Roboflow is an end-to-end computer vision platform that helps you The dataset includes 1078 images.
by traffic sign
9871 images 29 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random brigthness adjustment of between -25 and 0 percent * 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 20 21 22 23 24 25 26 27 28 ============================== For state of the art Computer Vision training notebooks you can use with this dataset,
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
by wellwise
9720 images 20 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 ============================== Food-items are annotated in YOLOv8 format. For state of the art Computer Vision training notebooks you can use with this dataset, No image augmentation techniques were applied. NutriScan - v2 2024-02-05 5:07pm Roboflow is an end-to-end computer vision platform that helps you The dataset includes 9736 images. The following pre-processing was applied to each image: This dataset was exported via roboflow.com on February 5, 2024 at 11:40 AM GMT To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com visit https://github.com/roboflow/notebooks