GitHub topics: efficientnetb0

Repositories

ShaikhBorhanUddin/Facial-Emotion-Recognition

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Nowrin-Sanjana/EfficientDeepLearningModelsForAlzheimer-sStageDetectionUsingMRI_Images

This repository contains the implementation and documentation of deep learning models designed to classify the stages of Alzheimer's disease using MRI images. The project explores the effectiveness of pre-trained convolutional neural networks (CNNs) such as VGG16, ResNet50, and EfficientNetB0 in detecting and categorizing neurodegenerative disease

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janaghoniem/Fruits-Recognition-Using-Deep-Learning-with-Data-Augmentation

A deep learning project for classifying 130+ fruits using EfficientNet, ResNet, and MobileNet with custom augmentations and SE blocks. Built on the Fruits-360 dataset.

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HarithKavish/SkinNet-Analyzer

SkinNet Analyzer: A Deep Learning-Based Skin Disease Detection System - College Final Year (4th year) Project

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LuluW8071/ASL-Alphabet-Detection

American Sign Language Alphabet Detection in Real Time using OpenCV-Mediapipe with EfficientNetB0 in PyTorch

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himanshumahajan138/TransferVision

Transfer learning for image classification using pre-trained models like ResNet50, ResNet100, EfficientNetB0, and VGG16 in Keras. Fine-tunes the last layers, applies image augmentation, and evaluates with Precision, Recall, AUC, F1 score, and early stopping for improved performance.

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AliTahir-101/finnish-traffic-sign-recognition

Real-time Finnish traffic sign detection and classification using synthetic data, transfer learning, and advanced deep learning techniques.

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amiruzzaman1/Deepfake-Detection

Deepfake detection leveraging the OpenForensics dataset, a comprehensive resource for face forgery detection and segmentation research. The project explores various deep learning models and evaluates their performance in distinguishing between real and fake images of human faces.

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msikorski93/Alzheimer-s-Disease-Classification

A multi classification using scikit-learn and TensorFlow models on MRI scans of patient's brains.

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safwanshamsir99/Dust-Detection-CNN

Dust detection on solar photovoltaics panel using pre-trained CNN models

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Furkantsnb/CoalClassifie

CoalClassifier: A deep learning model for classifying coal types using EfficientNetB0-based transfer learning and fine-tuning techniques. This project is designed to accurately distinguish between Anthracite, Bituminous, Lignite, and Peat classes and is developed using TensorFlow/Keras

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osharma08/ovarian_cancer_morphology

Ovarian cancer cell line in coculture with fibroblasts cell line: morphology analysis

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nouhabenhamada/Image-Classification-Is-it-Real-or-generated-by-AI-

A Streamlit aaplication to distinguish between AI-generated and real images. Utilizing the TensorFlow framework, the core model is based on EfficientNetB0, which has been fine-tuned on a custom dataset containing both fake and real images.

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Xza85hrf/Flag_Prediction_Project

This application predicts the name of a country (or countries) based on an input flag image. It uses advanced image processing techniques and deep learning models built with PyTorch to classify flags accurately.

Language: Python - Size: 24 MB - Last synced at: 3 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

tmfreiberg/HAM10000-skin-lesion-classification

HAM10000 Skin Lesion Classification

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sitamgithub-MSIT/MalDetect

A Deep Learning application for Malaria Detection

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kzaleskaa/depth-estimation-with-compression

Development of a depth estimation model based on a UNET architecture - connection of Bi-directional Feature Pyramid Network (BIFPN) and EfficientNet.

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Aku02/AI_CPS_IITR

TFlite+CNN

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harshdeepkalita/BarkRescue

I'm developing an app named BarkRescue, which includes project code, app functionalities, and system architecture. Additionally, I've written three detailed blogs on EfficientNet, YOLOv5, and MobileNet-v2, focusing on their architecture and workings before integrating these models into my project.

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Dev-Khant/Image-Captioning

Image Captioning using EfficientNet and GRU

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RidaDogrul/EfficientNetB0_for_transfer_learning

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AlvinManojAlex/MStar-SAR Fork of KaushalBarhate/MStar-SAR

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Etienne-bobo/food101_classification_tensorflow

The model employs mixed-precision training within the TensorFlow framework, utilizing transfer learning techniques that encompass both feature extraction and fine-tuning stages. This approach is executed on the EfficientNetB0 architecture

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GitUser8888/Predicting-Popularity-in-Korean-Drama-Industry-Based-on-Face-Image

This is my capstone project that was completed as part of the General Assembly Data Science Immersive curriculum. I conceptualized the idea, executed the project and developed a prototype for predicting success in the South Korean drama industry based on face image. This serves as a proof-of-concept which can definitely be developed further.

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