PyTorch implementation of Conditional Generative Adversarial Networks (cGAN) for image colorization of the MS COCO dataset
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Updated
Mar 15, 2024 - Python
PyTorch implementation of Conditional Generative Adversarial Networks (cGAN) for image colorization of the MS COCO dataset
Creates subsets of ImageNet (e.g. ImageNet100)
[TPAMI-22] Bottom-up, voting based video object detection method
Code for the paper "A Study of Face Obfuscation in ImageNet"
Simple application of VGG16 for the recognition of images, obtained from LFW, of a limited number of famous(15) with good performance (greater than 80%)
SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
This product uses ML technologies to compare the visual similarities (geometrical, color, etc) of images. It is intended to be used on ecommerce sites as a product recommender system. Models are trained with the VGG16 model which is pretrained on the imagenet dataset.
ImageNet-1K data download, processing for using as a dataset
This repository contains the source code of our work on designing efficient CNNs for computer vision
Artificial Intelligence in Assistive Technology. Using AI and Machine Learning we can redefine what vision means for visually impaired or blind.
Object Detection for Video with MXNet and GluonCV using YOLOv3
Scripts for building the ILSVR classification and localization training, validation, and testing data sets
PyTorch Implementation of SOTA SSL methods
ImageNet file xml format to Darknet text format
Explain Neural Networks using Layer-Wise Relevance Propagation and evaluate the explanations using Pixel-Flipping and Area Under the Curve.
VGG 19 is a Very Deep Convolutional Networks for Large-Scale Image Recognition In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convoluti…
A PyTorch implementation of universal adversarial perturbation (UAP) which is more easy to understand and implement.
MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks. In NeurIPS 2020 workshop.
"Exploring Simple Siamese Representation Learning" PyTorch implementation
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