Cifar with Noisy from Human or Synthesis
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Updated
May 21, 2024 - Python
Cifar with Noisy from Human or Synthesis
Python toolkit for speech processing
Sandbox for training deep learning networks
The official implementation of "Asymmetric Patch Sampling for Contrastive Learning"
The official implementation of paper: "Inter-Instance Similarity Modeling for Contrastive Learning"
Training ImageNet / CIFAR models with sota strategies and fancy techniques such as ViT, KD, Rep, etc.
Experience CIFAR-Net, a streamlined Python solution for classifying CIFAR-10 images with precision. Train, test, and predict effortlessly using our efficient CNN architecture and automation scripts. Dive into diverse datasets, make accurate predictions, and redefine image classification with ease! 🌟
The official implementation of LumiNet: The Bright Side of Perceptual Knowledge Distillation https://arxiv.org/abs/2310.03669
🎯 Deep Learning Framework for Image Classification & Regression in Pytorch for Fast Experiments
Improved CNN Training and Visualization
Implementaiton of BSC-Densenet-121 in Pytorch from research paper "Adding Binary Search Connections to Improve DenseNet Performance".
One-offs.
Neural network library written in C and Javascript
The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_DOT_A_Distillation-Oriented_Trainer_ICCV_2023_paper.pdf
✨基于卷积神经网络(CNN)和CIFAR10数据集的图像智能分类 Web 应用 Intelligent Image Classification Web Applcation based on Convolutional Neural Networks and the CIFAR10 Dataset✨🚩 (with README in English) 📌含在线demo:图像分类可视化界面,快速部署深度学习模型为网页应用,Web预测系统,决策支持系统(DSS),图像分类前端网页,图像分类Demo展示-Pywebio。AI人工智能图像分类-Pytorch。CIFAR10数据集,小模型。100%纯Python代码,轻量化,易复现
Connection Reduction of DenseNet for Image Recognition
VehicleVision leverages AWS services to train and deploy an image classification model that can differentiate between bicycles and motorcycles.
This repository includes a study that aims to apply classification on well-known CIFAR10 dataset. Detailed info in ReadMe
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