Topic: densenet169 Goto Github
Some thing interesting about densenet169
Some thing interesting about densenet169
densenet169,This repository hosts the Cervical Cancer Image Classification project, a comprehensive effort aimed at improving the classification accuracy of Squamous Cell Carcinoma (SCC) through advanced deep learning models and ensemble techniques. The project utilizes the Herlev dataset.
User: aditya1jhaveri
densenet169,Static malware detection using transfer learning techniques on MMCC_2015 dataset.
User: anagh-sharma
densenet169,Exploring the Application of Attention Mechanisms in Conjunction with Baseline Models on the COVID-19-CT Dataset
User: d4nh5u
densenet169,Skin Cancer Detection Web App using Flask Framework deployed on the Heroku server.
User: hassanejazul786
Home Page: https://skin-cancer-detector.herokuapp.com
densenet169,Analysis of Abnormality in Humerus X-Ray images using DenseNet
User: hawk453
densenet169,Image classification using deep learning models
User: hiramohammedk
densenet169,This repo was developed for the Alzheimer disease diagnosis.
User: javaidiqbal11
densenet169,To predict if a person has pneumonia or not using Chest X-Ray.
User: maanavshah
densenet169,Replicated results from DenseDepth using DenseNet169 in Python.
User: masterskepticista
densenet169,Notebooks made for the Kaggle Global Wheat Detection Competition
User: nitishas-812k
densenet169,Ensemble based transfer learning approach for accurately classifying common thoracic diseases from Chest X-Rays
Organization: perxeptron
densenet169,Models Supported: DenseNet121, DenseNet161, DenseNet169, DenseNet201 and DenseNet264 (1D and 2D version with DEMO for Classification and Regression)
User: sakib1263
densenet169,https://www.youtube.com/watch?v=Qmjg0hL7axs
User: satmm
densenet169,Towards Online Waypoint Generation for a Quadrotor Using Enhanced Monocular Depth Estimation.
User: savnani5
densenet169,🤳🧱 What the F.. Brick ?! A powerful tool that detects up the Lego bricks out of your picture !
User: thomas-rooty
Home Page: https://github.com/thomas-rooty/lego-detector
densenet169,Trabajo Fin de Máster: Estudio comparativo de un clasificador de imágenes en Raspberry Pi, de forma que se compara el tiempo de la inferencia en la Raspberry Pi con y sin el Neural Compute Stick (NCS). También se estudia como la complejidad de una red neuronal repercute en el tiempo de inferencia y se analiza si los tiempos obtenidos con el NCS en la Raspberry Pi se igualan a los conseguidos por la CPU del portátil y a los de una GPU de Google Colab.
User: valderas7
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