Emotion recognition with Keras library. Uses AffectNet dataset and valence-arousal labels. Implements CNN architecture with regression

Finding key points on the face

Facial key-points detection by using CNN model.

A simple guide to a vanilla CNN for regression, potentially useful for engineering applications.

This is my first project on Github

Fish scales constitute a valuable source of information about individual life histories, but correctly extracting this information requires a highly skilled expert. Here, we train a deep convolutional neural network architecture EfficientNet B4 on a set of about 9000 salmon scale images, and show that it attains good performance on predicting a …

Implementation of a convolutional neural network for regression and classification tasks

This project aims to enhance the quality of low-resolution images by mainly focusing on sharpening the edges of colors in the image; making them sharp and distinctly better quality with some improvement in the overall quality of the image. This will be achieved through Deep Learning.

INTRA-HOUR SOLAR IRRADIANCE ESTIMATION USING INFRARED SKY IMAGES AND MOBILENETV2-BASED CNN REGRESSION

Reproducing Brain Aging paper using the PyTorch libarary.

ENHANCING INTRA-HOUR SOLAR IRRADIANCE ESTIMATION THROUGH KNOWLEDGE DISTILLATION AND INFRARED SKY IMAGES

A CNN Regression Model for Predicting Age from an Image

The dataset used for the "A non-contact SpO2 estimation using video magnification and infrared data" publication

🖼️🔄 Combine CNNs with Fourier Transform techniques to enhance image quality by effectively reducing noise in various imaging applications.

The findings in the present study will be a breakthrough for the estimation of CPC concentration from S. platensis solely based on the information provided in the image without the need to perform a prior extraction process and identification of CPC concentration using analytical equipment.

Evaluate the robustness and performance between ML and DL models in predicting the CPC concentration under various image capturing devices, types of input image datasets, and lighting conditions. The findings in our current study can overcome the bottleneck by eliminating the need for laborious manual extraction processes and reducing the time and

House price estimation from visual and textual features using both machine learning and deep learning models