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- Shelf Detection - v1 2025-07-07 5:26am
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- ==============================
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- This dataset was exported via roboflow.com on July 6, 2025 at 10:32 PM GMT
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- Roboflow is an end-to-end computer vision platform that helps you
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- * collaborate with your team on computer vision projects
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- * collect & organize images
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- * understand and search unstructured image data
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- * annotate, and create datasets
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- * export, train, and deploy computer vision models
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- * use active learning to improve your dataset over time
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- For state of the art Computer Vision training notebooks you can use with this dataset,
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- visit https://github.com/roboflow/notebooks
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- To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
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- The dataset includes 251 images.
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- Shelf are annotated in YOLO v5 PyTorch format.
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- The following pre-processing was applied to each image:
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- * Auto-orientation of pixel data (with EXIF-orientation stripping)
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- * Resize to 640x640 (Stretch)
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- The following augmentation was applied to create 3 versions of each source image:
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- * 50% probability of horizontal flip
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- * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
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- * Randomly crop between 0 and 25 percent of the image
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- * Random rotation of between -15 and +15 degrees
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- * Random shear of between -15° to +15° horizontally and -15° to +15° vertically
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- * Random brigthness adjustment of between -25 and +25 percent
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- * Random exposure adjustment of between -15 and +15 percent
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- * Random Gaussian blur of between 0 and 1.5 pixels
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- * Salt and pepper noise was applied to 1.88 percent of pixels
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