19 skills found
edosedgar / MtcnnattackThe first real-world adversarial attack on MTCNN face detetction system to date
BingzheWu / Object Detetction ToolsNo description available
Yusin2Chen / Self Supervised Change DetetctionNo description available
developerrahulofficial / Gesture DetetctionNo description available
927621BAD019 / VARICOSE VEIN DETETCTION USING CNN ALGORITHMNo description available
Balakishan77 / Yolov8 Custom Object DetetctionThis repo explains the custom object detection training using Yolov8.
karjolamit / Radar Target Generation And DetetctionBased on Radar System Requirements, a radar target generation and detection system is developed using FMCW waveform configuration, signal propagation technique, Range/Doppler FFT method and finally 2D CFAR implementation .
romario076 / CryptoArbitrageReal time cryptocurrency intermarket arbitrage detection
shyamfec / CloudXNetCloud detetction from Satelllite Images
ismoilovdevml / Cancer DetectionCancer Detetction Machine Learning Model
khakhulin / IntrusionDetectionIntrusion Detetction( KDD99)
solanki1993 / Glaucoma DetetctionGlaucoma Detection and Classification using Deep Learning Glaucoma is a condition of eye in which optic nerve is damaged due to abnormally high pressure in the eye. It is a chronic and irreversible disease. It is one of the leading cause of blindness across the globe in people over the age of 60. There is no cure for glaucoma, but early detection and medical treatment can prevent from disease progression. A goal of this project was to use deep learning architecture to build a model to detect and classify glaucoma by combining multiple deep features. Keras was used to build the model. We used publicly available database Drishti-GS1. Methodology: This project was divided into two parts: Glaucoma Detection First, ROI (Region of interest) which is an area where optic disc and cup are located in the center and blood vessels of the Glaucoma fundus images were extracted using U shape convolutional neural network and then cup to disc ratio was calculated to classify if the image was glaucomatous or normal. This Paper was used for ROI extraction and disc segmentation. Glaucoma Classification Cup to disc ratio was used for glaucoma classification. VGG16 CNN model was used to distinguish between glaucoma and non-glaucoma related images from fundus images. Glaucoma severity can also be classified from cup to disc ratio: Mild ( CDR >0.3 and <0.5) Moderate (CDR >=0.5 and <0.8) Severe (CDR >=0.8)
souvikm99 / Flutter Yolov5 Realtime Object Detetction On CustomDataThis repo helps you to use yolov5 train on custom data, and export that into .pytorch, and use it with flutter to do object detection on static image and live video feed.
wxk2008 / Face Emotion Detectionusing the libsvm, dlib to do face emotion detetction, if you want to improve the accuracy rate, you should tarin much more using much more data
gagan16 / Fall Detetction Using Imu SThis project is machine learning project that detect falls in individual using reading from accelerometer and gyroscope
anandaparna126 / Cervical Cancer DetetctionCervical Cancer Detection using Deep learning CNN.
singh-apoorva1510 / DDoS Detection N PreventionDDoS Detetction and Prevention using Mininet and POX Controller
sachinsharma9780 / Master Thesis Free Space Detetction For Autonomous Driving Using Deep LearningNo description available
Akshay-A-Kulkarni / Twitter Graph ClusteringCommunity detection algorithms on custom twitter hashtag networks
