Related Objects of Interest: unlabeled
Top 10 Datasets and Models
The datasets below can be used to train fine-tuned models for 10 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 10 datasets below.
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
3764 images 6 classes
by enter
2166 images 11 classes
1 - Наличие двери 10 - Должна быть раковина (для определения %) 11 - Мусор 2 - Дверной проем пустой (для расчета % дверей) 3 - Наличие унитаза 4 - Наличия ванны 5 - Наличие готовой электрической розетки или выключателя 6 - Наличие не готовой электрической розетки или выключателя 7 - Наличие кухни 8 - Наличие батареи 9 - Должен быть унитаз (для определения %)