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。
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Updated
Jan 3, 2019 - Jupyter Notebook
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
AI-powered web application for real-time plant leaf disease detection using a fine-tuned ResNet-34 CNN, built with PyTorch and Flask.
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..
Image Segmentation using Oxford Pets Dataset with the goal to improve animal tranquilizer aiming system.
This is an implementation of ResNet using keras.
A ResNet-34-based image classification project for four package barcode no-read error types
Detecting Action performed in a video using resnet34 for spatial and temporal stream
Top 5% on Kaggle leaderboard using fast.ai library and resnet50 along with transfer learning.
Deep learning-based system for detecting synthetic facial manipulation in low-illuminance environments
Classifying waste types using transfer learning, with scripts for training, evaluation, and predictions
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.
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)
Lane detection using U-Net (ResNet34) with Unsupervised Domain Adaptation (entropy minimization) on the CARLANE / MoLane dataset. Includes training pipeline, model exports, and evaluation dashboard.
Automatically transform grayscale images into color photos using Deep Learning.
Classifies static images as wearing a face mask or not.
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