MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks. In NeurIPS 2020 workshop.

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

Pytorch Imagenet Models Example + Transfer Learning (and fine-tuning)

ImageNet file xml format to Darknet text format

ImageNet-1K data download, processing for using as a dataset

VGG16 Net implementation from PyTorch Examples scripts for ImageNet dataset

SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression

A PyTorch implementation of universal adversarial perturbation (UAP) which is more easy to understand and implement.

Code for the paper "A Study of Face Obfuscation in ImageNet"

Object Detection for Video with MXNet and GluonCV using YOLOv3

We use pretrained networks VGGnet, AlexNet, GoogLeNet, ResNet which trained on the ImageNet dataset as a feature extractor to classify images.

A Distributed ResNet on multi-machines each with one GPU card.

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

Explain Neural Networks using Layer-Wise Relevance Propagation and evaluate the explanations using Pixel-Flipping and Area Under the Curve.

Android app containing an Image classifier based on transfer learning CNN using Tensorflow 1.4.1 on Stanford's Imagenet cars dataset

Deep Learning model which uses Computer Vision and NLP to generate captions for images

Creates subsets of ImageNet (e.g. ImageNet100)

A demo for mapping class labels from ImageNet to COCO.

[TPAMI-22] Bottom-up, voting based video object detection method

No description provided.

A novel architecture for enhancing image classification. Reference paper: https://arxiv.org/abs/2104.12294

simple pytorch pipeline for pretraining/finetuning vision models on imagenet-1k

This is a simple example of how to prepare an ImageNet submission to the evaluation server.

Scripts for building the ILSVR classification and localization training, validation, and testing data sets

PyTorch implementation of Conditional Generative Adversarial Networks (cGAN) for image colorization of the MS COCO dataset

No description provided.

This project aims to classify different types of fruits using deep learning. The objective is to build a model that can accurately identify the type of fruit based on images.

Artificial Intelligence in Assistive Technology. Using AI and Machine Learning we can redefine what vision means for visually impaired or blind.

in this repository we get familiar with the Transfer Learning idea on the ImageNet dataset, in addition, we see how we can employ this vision to implement VGG-19 which is one of the common models of CNNs.

Predict Image Content 🖼 using Tensorflow.js and ImageNet Dataset Model 💿