Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).
function :write_results()
if not detect any boxes ,"for cls in img_classes:"It won't be executed.Then the return value is that it has not been modified.
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State of the art object detection in real-time using YOLOV3 algorithm. Augmented with a process that allows easy training of the classifier as a plug & play solution . Provides alert if an item in an alert list is detected.
Deep visual mining for your photos and videos using YOLOv2 deep convolutional neural network based object detector and traditional face recognition algorithms
Module for detecting traffic lights in the CARLA autonomous driving simulator. Based on the YOLO v2 deep learning object detection model and implemented in keras, using the tensorflow backend.
neural_cam module turn your usb camera into a smart camera that can detect objects it trained on. At the core is the state of the art deep learning (CNN) based on Darknet framework developed by Joseph Redmon.
function :write_results()
There are these changes.
if not detect any boxes ,"for cls in img_classes:"It won't be executed.Then the return value is that it has not been modified.
i in