Top One Datasets and Models
The datasets below can be used to train fine-tuned models for one 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 one datasets below.
by SOVAR
5359 images 13 classes
by MAIN
6115 images 13 classes
125 images 38 classes
7352 images 18 classes
Altera Analog Devices Bel fuse inc Fairchild Semiconductor General Semiconductors Industries Inc Holtek Semiconductors Level One Linear Technologies Micron Technologies Mitsubishi Electric Corporation Motorola Semiconductor Products Inc Nvidia ON Semiconductors Pericom Semiconductors Realtek Semiconductors STMicroelectronics Texas Instruments Inc Xilinx
7352 images 18 classes
Altera Analog Devices Bel fuse inc Fairchild Semiconductor General Semiconductors Industries Inc Holtek Semiconductors Level One Linear Technologies Micron Technologies Mitsubishi Electric Corporation Motorola Semiconductor Products Inc Nvidia ON Semiconductors Pericom Semiconductors Realtek Semiconductors STMicroelectronics Texas Instruments Inc Xilinx
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
264 images 239 classes