subject: deep learning type: dataset

Total is 13 Results
Aerial Fluvial Image Dataset (AFID) for Semantic Segmentation


Li-Fan Wu, Nina Mahmoudian, 0000-0002-3285-8234, Zihan Wang


816 2K/2.7K per-pixel annotated images with 8 classes: River, Boat, Bridge, Sky, Forest vegetation, Dry sediment, Drone self and Obstacle in river. Fluvial scenes are from Wabash River and Wildcat Creek in Indiana, USA.

Artificial Neural Network (ANN) autonomos vehicles Collision Avoidance deep learning drone Image Dataset Mechanical Engineering navigation RGB image dataset River Robotics semantic segmentation Unmanned Aerial Vehicle Wabash River

River Obstacle Segmentation En-route By USV Dataset (ROSEBUD)


Jalil Francisco Chavez Galaviz, Jianwen Li, Nina Mahmoudian, 0000-0002-3285-8234, Reeve David Lambert, 0000-0002-0259-2826, Zihan Wang


This dataset contains stills from video taken on Sugar Creek and the Wabash River in the US state of Indiana. Images are hand annotated to provide training and testing data for semantic segmentation networks.

autonomous and connected vehicles deep learning Mechanical Engineering River Robotics semantic segmentation

Raw Data and Results from Automatic Murine Cardiac Ultrasound and Photoacoustic Image Segmentations


Craig J Goergen, 0000-0001-8883-7953, Hayley Chan, Katherine Leyba, 0000-0003-3548-3391, Olivia Claire Loesch, Pierre Sicard, 0000-0001-5837-3916


Accuracy and dice scores from cross-validation reported in the cross-validation results spreadsheet. Radial strain raw data and results reported in strain results spreadsheet. Oxygen saturation values reported in sO2 results spreadsheet.

cardiovascular deep learning Imaging photoacoustic imaging ultrasound

Wheat spike blast image classification using deep convolutional neural networks


Carlos Gongora, Christian D Cruz, 0000-0002-2287-6754, Darcy Telenko, Jian Jin, Mariela Fernandez-Campos, 0000-0001-7736-9939, Mohammad Jahanshahi, Tao Wang, Yuting Huang


The folder includes i) a wheat spike blast image classification CNN model trained to automatically quantify and classify disease severity, ii) the generated datasets that include images of wheat spike blast severity levels under controlled...

Blast Botany and Plant Pathology CNN deep learning disease Magnaporthe oryzae plant disease phenotyping Plant phenotyping Python Wheat wheat blast

Visualized layer-wise visual features in deep residual neural network


Haiguang Wen, Junxing Shi, Wei Chen, Zhongming Liu, 0000-0002-8773-4204


Deep residual neural network is a brain-inspired computational model. 50 layers of neuron-like computational units are stacked into a bottom-up hierarchy. Features encoded at units are visualized for intuitively understanding the internal...

Biomedical Engineering deep learning natural vision Neural encoding Object Recognition

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