Related Objects of Interest: ==============================, the following pre-processing was applied to each image:, rebar exposure, crack, * 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
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Top Exposure Datasets and Models
The datasets below can be used to train fine-tuned models for exposure 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 exposure datasets below.
by Ahmed
716 images 4 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
30 images 10 classes
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
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
840 images 30 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 3 pixels * Random exposure adjustment of between -20 and +20 percent * Random rotation of between -3 and +3 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 23 24 25 26 27 28 29 ==============================
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
1196 images 3 classes
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:
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 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
9949 images 39 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 5 pixels * Random exposure adjustment of between -20 and +20 percent * Random rotation of between -10 and +10 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 23 24 25 26 27 28 29 30
by Work
4514 images 194 classes
* 50% probability of horizontal flip * Auto-contrast via histogram equalization * 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 * Random brigthness adjustment of between -25 and +25 percent * Random exposure adjustment of between -16 and +16 percent * Random rotation of between -12 and +12 degrees * Random shear of between -12° to +12° horizontally and -10° to +10° vertically * Randomly crop between 0 and 20 percent of the bounding box * 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 100 101 102
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