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.
American Sign Language Alphabet Detection in Real Time using OpenCV-Mediapipe with EfficientNetB0 in PyTorch
A state-of-the-art, open-source deepfake detection system built with PyTorch and EfficientNet-B0, featuring a user-friendly web interface for real-time image and video analysis.
🔪 Elimination based Lightweight Neural Net with Pretrained Weights
An implementation of the Arabic sign language classification using Keras on the zArASL_Database_54K dataset
Development of a depth estimation model based on a UNET architecture - connection of Bi-directional Feature Pyramid Network (BIFPN) and EfficientNet.
EDUNET FOUNDATION I SHELL I ARTIFICIAL INTELLIGENCE I 4-WEEKS VIRTUAL INTERNSHIP
Know Your Sight is an AI-powered web platform designed to make eye disease detection faster, smarter, and more accessible for everyone.
Image Captioning using EfficientNet and GRU
HAM10000 Skin Lesion Classification
Dust detection on solar photovoltaics panel using pre-trained CNN models
Automatic License Plate Detection and Recognition System for Challenging Bangladeshi License Plate using YOLOv8, fine-tuned EfficientNetB0 model and EasyOCR hybrid recognition.
The purpose of Food Vision project is to classify 101 variety of food items using Machine Learning.
SkinNet Analyzer: A Deep Learning-Based Skin Disease Detection System - College Final Year (4th year) Project
Benchmarking CNNs and Vision Transformers on CIFAR-10/100 using a unified PyTorch pipeline with transfer learning and model fusion.
A multi classification using scikit-learn and TensorFlow models on MRI scans of patient's brains.
AI-based Tuberculosis (TB) detection system using lung segmentation (ResUNet++), classification (EfficientNetB0), and explainable AI (Grad-CAM) for transparent, accurate, and reliable diagnosis from chest X-ray images.
CoalClassifier: A deep learning model for classifying coal types using EfficientNetB0-based transfer learning and fine-tuning techniques. This project is designed to accurately distinguish between Anthracite, Bituminous, Lignite, and Peat classes and is developed using TensorFlow/Keras
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