Implementation of popular deep learning networks with TensorRT network definition API
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Updated
Jun 7, 2024 - C++
Implementation of popular deep learning networks with TensorRT network definition API
Neural network that identifies and labels vegetables
This project's aim is to categorize ecommerce products from their images. MobileNetV2 model fine-tuned with 18K retail product images accross 9 categories. Project deployed with Flask and containerized via docker
This is a project focused on identifying the presence of pneumonia in chest X-ray images. Each image can be classified into one of three categories: Bacterial Pneumonia, Viral Pneumonia, or Normal.
Comparative Analysis of Deep Learning Models for Pneumonia Detection in Chest X-ray Images: A Game Changer in Improving Diagnosis and Patient Outcomes
Butterfly Classifier Inference API. Finetuned using MobileNetV2.
An Image Classification project w/ MobileNetV2 and DenseNet-121. Leveraging techniques like Hyperparameter Tuning, Transfer Learning, Imagine Preprocessing Techniques and Ensemble Methods.
Este repositorio es el resultado de mi trabajo de fin de máster en Data Science & Business Analytics
Final Year project developed using MobileNetV2 and implemented REST API using Django
KAN
This Python script calculates the similarity between a base image and a dataset of images using structural similarity and color histogram comparison. The results are sorted by similarity, can be showed with matplotlib and saved to a JSON file.
Utilize a MobileNetV2 encoder and Pix2Pix decoder to perform precise semantic segmentation, distinguishing objects in images, such as identifying flooded areas in flood images. The purpose is to enable accurate object delineation for applications like disaster response and environmental monitoring.
This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO
Light-weight Single Person Pose Estimator
Breast Cancer H&E classification of Images and Image Generation
2024-1 XAI611 고급빅데이터분석 Project Proposal
MoodSculpt is a project that analyzes user emotions using facial recognition technology and suggests songs and movies based on the detected mood. It utilizes Transfer Learning, Spotify API for music recommendations, and OMDB API for movie recommendations.
A project requirement for the subject 'CS333-M - Data Analytics'
Image forgery detection using CNN fusion model achieving 85% test accuracy. Features ELA preprocessing and fusion of InceptionV3, VGG16, and MobileNetV2. Ideal for digital forensics.
This is an ongoing project of applying evolutionary computing to TensorFlow.js as alternative to their local search default routines
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