Backgroundaim to train and validate the prediction performance of the deep learning dl model to predict visual field vf in central 10 from spectral domain optical coherence tomography sd oct. Create a deep neural network in which the input is pixel matrix.
Using Human Brain Activity To Guide Machine Learning
Body measurement deep learning. Use the neural network to identify various feature value weight x height yetc from neural network. Because weight decay is ubiquitous in neural network optimization the deep learning framework makes it especially convenient integrating weight decay into the optimization algorithm itself for easy use in combination with any loss function. The confidence and affinity maps are parsed by greedy inference to produce the 2d keypoints for all people in the image. The authors of the paper have shared two models one is trained on the multi person dataset mpii and the other is trained on the coco dataset. This architecture won the coco keypoints challenge in 2016. Use machine learning to train deep neural net to identify feature value.
22 pre trained models for human pose estimation. Aim of this study was to develop and evaluate a software toolkit which allows for a fully automated body composition analysis in contrast enhanced ab. Methods this multicentre cross sectional study included paired humphrey field analyser hfa 10 2 vf and sd oct measurements from 591 eyes of 347 patients with open angle glaucoma oag or. Using the front end camera of any mobile device sizers proprietary computer vision technology precisely calculates a persons body measurements with the highest accuracy and utilizes deep learning algorithms to determine correct size recommendations for best fitting clothing. If you do this successfully you are succeeded in your task.
Gallery of Body Measurement Deep Learning
