BharathBarry99

Dynamic-Convolutional-Attention-for-Classification-of-Diabetic-Retinopathy

This repository utilizes deep learning to diagnose Diabetic Retinopathy (DR). By combining a ResNet50 CNN model trained with DCA and a weighted average ensemble technique, it accurately classifies fundus images. The approach improves DR screening accuracy, benefiting clinicians and patients. Documentation facilitates easy implementation.

Object_detection_fridge

Detects object present inside a fridge using yolov7

Nike_reviews_scrapper

Scrap Nike reviews using Beautifulsoup and Docker.

Leetcode-Problems

Brain-Tumour-classification-using-CNN

Using keras, tensorflow and openCV , my model can classify mri images of brain as tumorous and non tumorous.

Traffic-Sign-Classification-using-CNN

Classify Traffic signs using CNN

Driver-Drowsiness-Detection-using-CNN

With the help of OpenCV, tensorflow and keras , the model can detect if the driver is drowsy on not and alert him if he gets drowsy

BharathBarry99

Config files for my GitHub profile.

Mask-R-CNN

Explaining the differences between traditional image classification, object detection, semantic segmentation, and instance segmentation is best done visually. When performing traditional image classification our goal is to predict a set of labels to characterize the contents of an input image (top-left). Object detection builds on image classification, but this time allows us to localize each object in an image. The image is now characterized by: Bounding box (x, y)-coordinates for each object An associated class label for each bounding box.Instance segmentation algorithms, on the other hand, compute a pixel-wise mask for every object in the image, even if the objects are of the same class label (bottom-right). Here you can see that each of the cubes has their own unique color, implying that our instance segmentation algorithm not only localized each individual cube but predicted their boundaries as well.