QAT(quantize aware training) for classification with MQBench
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Updated
Nov 18, 2021 - Python
QAT(quantize aware training) for classification with MQBench
This is a project documentation about melanoma detection methods using convolutional neural networks.
🔪 Elimination based Lightweight Neural Net with Pretrained Weights
An implementation of the Arabic sign language classification using Keras on the zArASL_Database_54K dataset
The purpose of Food Vision project is to classify 101 variety of food items using Machine Learning.
American Sign Language Alphabet Detection in Real Time using OpenCV-Mediapipe with EfficientNetB0 in PyTorch
49.5 mAP50 Detector enet4y2-coco.cfg = EfficientnetB0 + 4YOLO Layers + BiDirectionalFeatureMap with COCO Dataset and 81.0 mAP50 with VOC2007 test Dataset.
Image Captioning using EfficientNet and GRU
HAM10000 Skin Lesion Classification
Development of a depth estimation model based on a UNET architecture - connection of Bi-directional Feature Pyramid Network (BIFPN) and EfficientNet.
INR Denomination Recognition is an image classification project
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.
I'm developing an app named BarkRescue, which includes project code, app functionalities, and system architecture. Additionally, I've written three detailed blogs on EfficientNet, YOLOv5, and MobileNet-v2, focusing on their architecture and workings before integrating these models into my project.
Mask Monitoring System
A multi classification using scikit-learn and TensorFlow models on MRI scans of patient's brains.
A project on building deep learning classifier to classify playing cards
Dust detection on solar photovoltaics panel using pre-trained CNN models
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
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