49.5 mAP50 Detector enet4y2-coco.cfg = EfficientnetB0 + 4YOLO Layers + BiDirectionalFeatureMap with COCO Dataset and 81.0 mAP50 with VOC2007 test Dataset.

This is a project documentation about melanoma detection methods using convolutional neural networks.

TFlite+CNN

QAT(quantize aware training) for classification with MQBench

Image Captioning using EfficientNet and GRU

A project on building deep learning classifier to classify playing cards

INR Denomination Recognition is an image classification project

An implementation of the Arabic sign language classification using Keras on the zArASL_Database_54K dataset

Development and analysis of various deep NN models to detect glaucoma cases from fundus images. The performance of the best model was evaluated with cross-validation. Mean F1-score: 0.95975, with a standard deviation of 0.02274.

Mask Monitoring System

🔪 Elimination based Lightweight Neural Net with Pretrained Weights

Clasificación de imágenes y reconocimiento de objetos mediante la red neuronal convolucional CNN DenseNet y EfficientNet con el modelo frozen model y el framework Coffe. Posteriomente, mediante la red neuronal convolucional CNN MobileNet-SSD y YOLO con el framework TensorFlow

The purpose of Food Vision project is to classify 101 variety of food items using Machine Learning.

A multi classification using scikit-learn and TensorFlow models on MRI scans of patient's brains.

Implementation of a neural network for solving the problem of mushroom classification based on the TensorFlow library and the pre-trained efficientnet/b0 model.

Efficient way to detect if a face has a mask on. Used EfficientNet-B0 model architecture.

The model employs mixed-precision training within the TensorFlow framework, utilizing transfer learning techniques that encompass both feature extraction and fine-tuning stages. This approach is executed on the EfficientNetB0 architecture

This is my capstone project that was completed as part of the General Assembly Data Science Immersive curriculum. I conceptualized the idea, executed the project and developed a prototype for predicting success in the South Korean drama industry based on face image. This serves as a proof-of-concept which can definitely be developed further.