使用TensorFlow自己搭建一些经典的CNN模型，并使用统一的数据来测试效果。
Few-shot learning experiments mostly on speaker recognition.
This repository contains Final project of CSE428 Brac University
Explainable Speaker Recognition
U-Net segmentation algorithm with options of pretrained resnet34 and resnet50 encoders. All of the project dockerized with gpu suppport on anaconda environment with multiple loss support..
Top 5% on Kaggle leaderboard using fast.ai library and resnet50 along with transfer learning.
Detecting Action performed in a video using resnet34 for spatial and temporal stream
Image classifier based on Pytorch
Classifying waste types using transfer learning, with scripts for training, evaluation, and predictions
Knocking occurs when fuel burns unevenly in your engine. When everything is going as it should, and the cylinders have the correct mix of air and fuel, the mixture burns in a controlled, progressive manner. After each cylinder's air/fuel mixture burns, it should create a small “shock wave” in your engine. This project is a knocking prediction app.
AI based image classification inspired MobileNet V2 architecture by implementing changes in base architecture and details about using it as a quick response model (proposition) for rapid application as well as comparing it with other models for the same application.
Classifies static images as wearing a face mask or not.
MNIST classification with deeper CNN models
A Streamlit (Python Web Framework) Application that detects most common human activities from Pre-Recorded Videos or Live Camera Feed.
Deep CNN models ResNet34 and VGG16 have been trained and tested for image classification task using MNIST and CIFAR dataset (part of mini-project from the Deep learning and computer vision module)
CNN to recognize which of my cats appears in a photo.
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