Top Horizontal Computer Vision Models
The models below have been fine-tuned for various horizontal detection tasks. You can try out each model in your browser, or test an edge deployment solution (i.e. to an NVIDIA Jetson). You can use the datasets associated with the models below as a starting point for building your own horizontal detection model.
At the bottom of this page, we have guides on how to count horizontals in images and videos.
1169 images 69 classes
pole * 50% probability of horizontal flip * 50% probability of vertical flip * Random Gaussian blur of between 0 and 3.25 pixels * Randomly crop between 0 and 33 percent of the image * annotate, and create datasets * 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 21 22 23 24 25 26 27 28 29 30
by VESIT
48 images 7 classes
by ECE4300
1605 images 24 classes
object * 50% probability of horizontal flip * Resize to 640x640 (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 ============================== Animal detect - v2 2023-04-25 3:38pm Animals are annotated in YOLOv8 format. For state of the art Computer Vision training notebooks you can use with this dataset, Person dataset - v3 2023-11-16 1:52am Roboflow is an end-to-end computer vision platform that helps you The dataset includes 269 images. The following augmentation was applied to create 3 versions of each source image: The following pre-processing was applied to each image: This dataset was exported via roboflow.com on April 25, 2023 at 10:09 AM GMT To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
by kaushik
8015 images 78 classes
* 50% probability of horizontal flip * 50% probability of vertical flip * Random Gaussian blur of between 0 and 3.25 pixels * Randomly crop between 0 and 33 percent of the image * 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 100 120 21 22 23 24 25 26 27 28
173 images 4 classes
1148 images 4 classes
8389 images 127 classes
Acampar Aguas termales Alojamiento Altura máxima (Serie complementaria) Altura máxima Serie de límites máximos Anchos de vía Angostamiento de la vía izquierda Angostamiento en ambos lados Animales en vía Aproximación a redondel Aproximación a semáforo Ascenso pronunciado Asignación de carril anticipado de intersección Auxilio mecánico Bandas transversales de alerta Bifurcación a la derecha Camping / Verde Carril izquierdo cerrado Cascada Ceda el paso
980 images 4 classes
by gfd
1321 images 4 classes
by TIUS REERACH
1031 images 4 classes
1050 images 41 classes
by gfd
2196 images 4 classes
by ingredients
9335 images 38 classes
* 50% probability of horizontal flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise * Random rotation of between -15 and +15 degrees * Randomly crop between 0 and 20 percent of the image * 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 23 24 25 26 27 28 29 30
by tiu research
1768 images 4 classes
1863 images 4 classes
by tiu research
653 images 4 classes
by Tius
2467 images 4 classes
by Tius
4947 images 4 classes
140 images 2 classes
by EfigasV2
3958 images 15 classes
1717 images 4 classes
131 images 6 classes