Related Objects of Interest: * annotate, and create datasets, * auto-orientation of pixel data (with exif-orientation stripping), * collaborate with your team on computer vision projects, * equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down, * export, train, and deploy computer vision models, * use active learning to improve your dataset over time, ==============================, the following pre-processing was applied to each image:, morning star, * collect & organize images
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Top Upside Datasets and Models
The datasets below can be used to train fine-tuned models for upside 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 upside datasets below.
1480 images 8 classes
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 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 DuoKan
3999 images 20 classes
Advance Block Evening Doji Star Evening Star Hikkake Pattern Identical Three Crows Ladder Bottom Morning Doji Star Morning Star Rising-Falling Three Methods Stick Sandwich Tasuki Gap Three Advancing White Soldiers Three Black Crows Three Inside Up-Down Three Line Strike Three Outside Up-Down Tristar Pattern Unique 3 River Up-Down Gap Side-by-side White Lines Upside-Downside Gap Three Methods
37 images 3 classes
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 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
43 images 39 classes
Bearish Counterattack Line Bearish Engulfing Bearish Harami Bearish Marubozu Bearish Spinning Top Bullish Counterattack Lines Bullish Engulfing Bullish Harami Bullish Marubozu Bullish Spinning Top Dark Cloud Cover Downside Tasuki Gap Dragonfly Doji Evening Star Falling Three Methods Falling Window Four Price Doji Gravestone Doji Hammer Hanging Man
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 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
9857 images 26 classes
Advance Block Dragonfly Doji Evening Doji Star Evening Star Gravestone Doji Hammer Hanging Man Hikkake Pattern Identical Three Crows Ladder Bottom Marubozu Morning Doji Star Morning Star Rising-Falling Three Methods Spinning Top Stick Sandwich Tasuki Gap Three Advancing White Soldiers Three Black Crows Three Inside Up-Down
by Deneme
3362 images 694 classes
* Auto-contrast via contrast stretching * 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 shear of between -14° to +14° horizontally and -15° to +15° vertically * Resize to 800x800 (Stretch) * Salt and pepper noise was applied to 1.13 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 100 101 102 103 104 105 106 107
by Deneme
3256 images 692 classes
* Auto-contrast via contrast stretching * 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 shear of between -14° to +14° horizontally and -15° to +15° vertically * Resize to 800x800 (Stretch) * Salt and pepper noise was applied to 1.13 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 100 101 102 103 104 105 106 107
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