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,, roboflow is an end-to-end computer vision platform that helps you
Top 516 Datasets and Models
The datasets below can be used to train fine-tuned models for 516 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 516 datasets below.
by none
1000 images 250 classes
25 images 170 classes
0 128 223 433 0 26 164 236 0 36 591 681 1000 1016 170 192 1003 1018 148 167 1015 1024 165 187 106 129 247 276 108 143 349 393 11 35 116 148 110 144 291 374 117 130 87 104 124 140 66 86 130 150 125 153 140 150 325 341 140 180 197 252 145 172 163 199 151 180 640 680 151 234 844 947 151 331 148 391 154 168 93 109
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
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
159 images 171 classes
125-697354497355 131-543498411017 148-45291005291 187-462433862434 196-131216931217 230-806349206349 247-060317460317 251-968369306144 254-645502645503 263-314285714286 277-401058201058 279-568253968254 281-73544973545 284-5107421875 293-655026455026 294-273454051907 305-574603174603 311-050847457627 317-053115068856 326-815826933263