Related Objects of Interest: ==============================, * 50% probability of horizontal flip, * auto-orientation of pixel data (with exif-orientation stripping), the following pre-processing was applied to each image:, roboflow is an end-to-end computer vision platform that helps you, * 50% probability of vertical flip, * collaborate with your team on computer vision projects, * collect & organize images, * equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down, * annotate, and create datasets
Top Flip Datasets and Models
The datasets below can be used to train fine-tuned models for flip 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 flip datasets below.
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
2801 images 16 classes
\ ` metal-big-flip-grey metal-big-slide-grey metal-small-round-green metal-small-round-grey mixed-plastic-small-green mixed-plastic-small-yellow recycle-flip-square-blue recycle-flip-square-green recycle-round-blue recycle-round-green recycle-round-yellow recycle-slide-square-blue recycle-slide-square-green recycle-slide-square-yellow
by TEST 1
2254 images 35 classes
chicken train #-Healthy-and-Sick-Chicken-Detection->-2023-02-04-2:29pm *-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 *-Random-exposure-adjustment-of-between--25-and-+25-percent *-Resize-to-416x416-(Stretch) *-annotate *-collaborate-with-your-team-on-computer-vision-projects *-collect-&-organize-images *-export *-understand-and-search-unstructured-image-data *-use-active-learning-to-improve-your-dataset-over-time 1WOC-are-annotated-in-Tensorflow-Object-Detection-format. 2024-at-10:31-AM-GMT ============================== For-state-of-the-art-Computer-Vision-training-notebooks-you-can-use-with-this-dataset Healthy-and-Sick-Chicken-Detection---v18-2023-02-04-2:29pm
1518 images 5 classes
by narsi simhn
112 images 7 classes
by school
518 images 1 classes
by ArtisanAI
494 images 1 classes
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 school
520 images 7 classes
* collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data ============================== Roboflow is an end-to-end computer vision platform that helps you This dataset was exported via roboflow.com on February 18, 2024 at 3:02 AM GMT flip-hue-brightness - v8279 2024-02-18 11:01am
52 images 51 classes
by WPI
84 images 5 classes
28 images 26 classes
Scratches back body damage back damage body damage body flip body scrape body scrat foot ares Dam front body light damage front damage head lamp and nose da head lampm and steering damage head light damage mud guard damage mud guard dent no damage nose damage scrape damage seat dama side mirror damage
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 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
by York
246 images 27 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 0.5 pixels * Random brigthness adjustment of between -16 and +16 percent * Random exposure adjustment of between -6 and +6 percent * Resize to 600x600 (Fit (white edges)) 15 16 17 18 19 20 21 ============================== Chocolates are annotated in YOLO v5 PyTorch format. Dark Marzipan It includes 267 images. Milk California Brittle
by test
200 images 22 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 600x600 (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 ============================== Chocolates 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 267 images. The following augmentation was applied to create 5 versions of each source image: The following pre-processing was applied to each image: This dataset was exported via roboflow.com on June 4, 2023 at 12:27 PM GMT To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
by yolo
200 images 23 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 600x600 (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 ============================== Chocolates Chocolates 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 267 images. The following augmentation was applied to create 5 versions of each source image: The following pre-processing was applied to each image: This dataset was exported via roboflow.com on June 4, 2023 at 12:27 PM GMT
by chocolate
244 images 31 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 0.5 pixels * Random brigthness adjustment of between -16 and +16 percent * Random exposure adjustment of between -6 and +6 percent * Resize to 600x600 (Fit (white edges)) -black chocolate -brown chocolate -gift chocolate -wave chocolate -white chocolate 15 16 17 18 19 20 21
by nina
245 images 31 classes
chocolate * 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 0.5 pixels * Random brigthness adjustment of between -16 and +16 percent * Random exposure adjustment of between -6 and +6 percent * Resize to 600x600 (Fit (white edges)) 15 16 17 18 19 20 21 ============================== Chocolates are annotated in YOLO v5 PyTorch format. It includes 267 images. The following augmentation was applied to create 5 versions of each source image: