We are a silicon valley based AI company working on applying deep learning and computer vision to automate food quality assessment - improving food quality and reducing food waste.
Build a baseline model for a selected problem statement in 3-4 months using's AgShift existing deep learning framework.
Skills: The ideal person should have worked with at least one of the recent architectures like ResNet, Fast/Faster R-CNN, SSD, Yolo, MobileNets to perform object detection/localization and image segmentation. He/She should be knowledgeable about the intricate details of convolution and should have created own models either from scratch or based on existing models like inception, vgg etc. The candidate should be extremely familiar with TensorFlow and the top level frameworks like TF-Slim, Keras. A broader background in other Machine Learning areas like regression and multinomial logistic regression and familiarity on taking the models to mobile platforms is highly desired. The person should have deep knowledge in Computer Vision and should be extremely familiar/hands-on implementing histogram equalization, adaptive equalization, contour detection, watershed algorithm, dilation, erosion techniques. The candidate should be proficient in coding, particularly using Python and C++.
About the recuiterMember since Mar 14, 2020 Deepak Chauhan
from Lebap, Turkmenistan