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- README.dataset.txt +6 -0
- README.roboflow.txt +33 -0
- data.yaml +10 -0
- test/images/image003_jpg.rf.31a58b72ba39b3303827410b12d4b2fd.jpg +3 -0
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- test/labels/image003_jpg.rf.31a58b72ba39b3303827410b12d4b2fd.txt +1 -0
- test/labels/image003_jpg.rf.7775fe400779c565de8b263ab61d7a88.txt +1 -0
- test/labels/image004_jpg.rf.554cf4a4c0caf4538253cce2ac5d18d0.txt +1 -0
- test/labels/image004_jpg.rf.6bfa753aefa6d74e2028b5cc33fd7a06.txt +1 -0
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- test/labels/image007_jpg.rf.5dba73ecab329994bf3e420fadb79273.txt +1 -0
- test/labels/image007_jpg.rf.6c880496497084da7fb11b26fe7a82d4.txt +1 -0
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- test/labels/image018_jpg.rf.fd23c831d5052c26ffdbec601457484a.txt +1 -0
- test/labels/image021_jpg.rf.513498f0f49e9ace8f8776b098283c81.txt +1 -0
- test/labels/image023_jpg.rf.0a37b72e6ba0202811c9dff51774a078.txt +1 -0
- test/labels/image026_jpg.rf.663008761e58ab6ed4129df116b38282.txt +1 -0
- test/labels/image032_jpg.rf.62846c1098962b4febc68d57f9480155.txt +0 -0
- test/labels/image037_jpg.rf.7d53f17a5c1d47e43dd22fbf6dec9dbf.txt +1 -0
- test/labels/image059_jpg.rf.766990443636ae3936f81c6f07325923.txt +1 -0
- test/labels/image087_jpg.rf.4b5f69b5283cfa3ab718c5d764c8faa7.txt +1 -0
- train/images/image001_jpg.rf.1efad0220841538c05b306434bfc0730.jpg +3 -0
- train/images/image001_jpg.rf.44d3ec0a2d838f8a6a820e249511c272.jpg +3 -0
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README.dataset.txt
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# Rupee Vision > rv-indian-coins-v1
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https://universe.roboflow.com/satyam-shorrf/rupee-vision
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Provided by a Roboflow user
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License: CC BY 4.0
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README.roboflow.txt
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Rupee Vision - v1 rv-indian-coins-v1
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==============================
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This dataset was exported via roboflow.com on December 21, 2025 at 3:28 AM 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 446 images.
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Coins are annotated in YOLOv8 Oriented Object Detection 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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* Random rotation of between -10 and +10 degrees
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* Random brigthness adjustment of between -15 and +15 percent
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* Random exposure adjustment of between -10 and +10 percent
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data.yaml
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train: train/images
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val: valid/images
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test: test/images
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names:
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0: 10-rupee
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1: 2-rupee
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2: 20-rupee
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3: 5-rupee
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4: coins
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test/images/image003_jpg.rf.31a58b72ba39b3303827410b12d4b2fd.jpg
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test/labels/image003_jpg.rf.31a58b72ba39b3303827410b12d4b2fd.txt
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0 0.865625 0.10468750000000004 0.15625 0.1046875 0.15625000000000006 0.840625 0.865625 0.840625
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test/labels/image003_jpg.rf.7775fe400779c565de8b263ab61d7a88.txt
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2 0.928125 0.13125000000000006 0.175 0.13125 0.17500000000000004 0.828125 0.928125 0.828125
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test/labels/image004_jpg.rf.554cf4a4c0caf4538253cce2ac5d18d0.txt
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0 0.8609375 0.23593750000000005 0.10312500000000005 0.2359375 0.10312500000000006 0.89375 0.8609375 0.89375
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test/labels/image004_jpg.rf.6bfa753aefa6d74e2028b5cc33fd7a06.txt
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3 0.725 0.3140625 0.19843750000000004 0.3140625 0.19843750000000007 0.7015625 0.725 0.7015625
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test/labels/image006_jpg.rf.7207f66313b7782ab7f14744029bfb8a.txt
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2 0.8984375 0.24218750000000006 0.19531250000000006 0.2421875 0.19531250000000006 0.8484375 0.8984375 0.8484375
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0 0.915625 0.22812500000000005 0.303125 0.228125 0.30312500000000003 0.875 0.915625 0.875
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2 0.95625 0.17343750000000008 0.2640625 0.17343750000000005 0.26406250000000003 0.8671875 0.95625 0.8671875
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3 0.909375 0.10468750000000009 0.1921875 0.10468750000000004 0.19218750000000004 0.8796875 0.909375 0.8796875
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1 0.94375 0.24531250000000004 3.004211679158351e-17 0.2453125 7.357448348002457e-17 0.95625 0.94375 0.95625
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2 0.9328125 0.11562500000000006 0.1203125 0.115625 0.12031250000000004 0.878125 0.9328125 0.878125
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1 0.7203125000000001 0.30625 0.240625 0.30625 0.24062500000000003 0.7859375 0.7203125000000001 0.7859375
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0 0.8546875 0.13125000000000003 0.09843750000000002 0.13125 0.09843750000000007 0.8796875 0.8546875 0.8796875
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4 0.6578125 0.33281250000000007 0.3609375 0.3328125 0.3609375 0.653125 0.6578125 0.653125
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4 0.6046875 0.353125 0.3421875 0.353125 0.34218750000000003 0.6265625 0.6046875 0.6265625
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