fire-smoke-detect-yolov4

ncnn is a high-performance neural network inference framework optimized for the mobile platform

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

mean Average Precision - This code evaluates the performance of your neural net for object recognition.

YOLO ROS: Real-Time Object Detection for ROS

implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)

GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2

License Plate Detection and Recognition in Unconstrained Scenarios

MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire: