Multi image label classification by multi models.
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Updated
Oct 15, 2020 - Python
Multi image label classification by multi models.
Classify the severity stages of Diabetic Retinopathy
A set of notebooks that leverage classical ML algorithms and DL neural nets using TF, Keras and Theano to address a series of issues in the field of conservation and biology.
This project is a web application that uses YOLOv5 and InceptionResNetV2 models for license plate detection and Optical Character Recognition (OCR) text extraction. The web applications were built using streamlit and flask
Gathering and labeling data for an image-text fusion model for flavor classification of food based on recipes. Generating images using stable diffusion models, and using deep classification models like BERT, BiLSTM, and InceptionResnet.
Explore my comprehensive collection of AI models for blood cancer detection. Leveraging deep learning and medical imaging, these models aim to revolutionize early diagnosis and treatment, making a significant impact on the battle against blood cancers. #AI #HealthcareInnovation
Explore diverse computer vision projects using Transfer Learning(TL), Convolutional Neural Networks (CNN), Autoencoder and more in this collaborative repository
In this repository you can find the jupyter notebooks used to take part at the competitions created for the Artifical Neural Networks and Deep Learning exam at Politecnico di Milano.
Deep Learning models for Object Detection
Image Caption Generation using Keras' Pre-Trained Image Feature Extraction models and LSTM
Address the crowd counting problem on the Mall dataset (sparse) by exploring regression-based (Xception) and density-based (CSRNet) approaches.
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.
This repository contains the jupyter notebooks used to take part at the competitions created for the Artifical Neural Networks and Deep Learning exam at Politecnico di Milano.
A Deep learning project for food classification on the popular Food-101 dataset using pre-trained CNNs like the Inception network, ResNets, and Inception-ResNets.
Malaria is one of the disease causing deaths worldwide. Detection of malaria is a physical task invloving pathologist to diagnosis the presence of parasite inside the microscopic view of thin blood smears. This process is prone to errors as it requires an experienced person. Also, eye sight plays a vital role in order to detect the malaria. Ther…
KeepCoding Bootcamp Big Data & Machine Learning - Práctica Deep Learning
Image classification over a very small non-uniform dataset.
Implementation of Google's Inception ResNet v2 for the task of automatic colorization of gray-scale images in the CIELAB color space.
fine-tuning an image classification problem with transfer learning, using inResNetV2
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