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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.
An Image Classification project w/ MobileNetV2 and DenseNet-121. Leveraging techniques like Hyperparameter Tuning, Transfer Learning, Imagine Preprocessing Techniques and Ensemble Methods.
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.
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.
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.