Visualizer for neural network, deep learning, and machine learning models
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Updated
Sep 18, 2020 - JavaScript
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Visualizer for neural network, deep learning, and machine learning models
ncnn is a high-performance neural network inference framework optimized for the mobile platform
YOLOv3 in PyTorch > ONNX > CoreML > iOS
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.
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
YOLO9000: Better, Faster, Stronger - Real-Time Object Detection. 9000 classes!
An open source tool to quantify the world
YOLO ROS: Real-Time Object Detection for ROS
A caffe implementation of MobileNet-YOLO detection network
Label images and video for Computer Vision applications
Real-time people Multitracker using YOLO v2 and deep_sort with tensorflow
A Python wrapper on Darknet. Compatible with YOLO V3.
This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our BMW-LabelTool-Lite and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. NoCode training with YOLOv4 and YOLOV3 has never been so easy.
Modeled Google and Bing to build a distributed search engine for the dark web
Convolutional Neural Networks
OSINT Tool For Scraping Dark Websites
Computer Vision and Image Recognition algorithms for R users
Support Yolov5s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.
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