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, * 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, ==============================, for state of the art computer vision training notebooks you can use with this dataset,, no image augmentation techniques were applied.
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Top 772 Datasets and Models
The datasets below can be used to train fine-tuned models for 772 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 772 datasets below.
45 images 2069 classes
mask person -0-0 -100-64878048780469 -1000-0022505626407 -1000-1762127875611 -1000-8264462809908 -1001-0032508127034 -1001-8777110212488 -1002-3880414131419 -1003-4712241544406 -1003-5545789548756 -1003-9399267094751 -1005-1809269702934 -1005-4692262573712 -1007-4300071058922 -1008-9999999999981 -1009-4100906134347 -1009-4150672768247 -1009-6934516720788
by Traffic
1958 images 1876 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 416x416 (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 100 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 101
529 images 4201 classes
* 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 * use active learning to improve your dataset over time 100 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 101 1010
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