cnn-classification

A Benchmark of Text Classification in PyTorch

基于法律裁判文书的事件抽取及其应用，包括数据的分词、词性标注、命名实体识别、事件要素抽取和判决结果预测等内容

Deep facial expressions recognition using Opencv and Tensorflow. Recognizing facial expressions from images or camera stream

This repository contains the source code of our work on designing efficient CNNs for computer vision

🔥 Reproducibly benchmarking Keras and PyTorch models

Potato Disease Classification - Training, Rest APIs, and Frontend to test.

This is official repository of our paper "RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification". Codes include a two stage model to achieve drone detection and classification using some FFT/STFT analytical method. The Raw data will be free to use after our paper is accept. Star us!!!!, if you think this is useful♥

Training and evaluating state-of-the-art deep learning CNN architectures for plant disease classification task.

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

A Complete and Simple Implementation of MobileNet-V2 in PyTorch

implement AlexNet with C / convolutional nerual network / machine learning / computer vision

CNN image classifier implemented in Keras Notebook 🖼️.

The source code and dataset are used to demonstrate the DF model, and reproduce the results of the ACM CCS2018 paper

Official implementation of ID-unaware Deepfake Detection Model

GroupSoftmax cross entropy loss function for training with multiple different benchmark datasets

This repository explores the variety of techniques and algorithms commonly used in deep learning and the implementation in MATLAB and PYTHON

Projects from the Deep Learning Specialization from deeplearning.ai provided by Coursera

A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework

PyTorch tutorials A to Z

1D GAN for ECG Synthesis and 3 models: CNN, LSTM, and Attention mechanism for ECG Classification.