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What is the ImageNet challenge?

What is the ImageNet challenge?

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions.

What is ImageNet used for?

The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided.

What is ImageNet large scale visual recognition?

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image classification at large scale. Another motivation is to measure the progress of computer vision for large scale image indexing for retrieval and annotation.

Does ImageNet generalize ImageNet?

Abstract: We build new test sets for the CIFAR-10 and ImageNet datasets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models’ inability to generalize to slightly “harder” images than those found in the original test sets. …

Can ImageNet be used commercially?

The competition rules says the license of anything(code/library/dataset/pretrained models) we are using should be available for commercial use. And it looks like imagenet is for non-commercial only. Also note that many pre-trained models in library model zoos(including torchvision.

Why is ImageNet so important?

It proved that training on ImageNet gave models a big boost, requiring only fine-tuning for other recognition tasks. Convolutional neural networks trained in this manner find patterns at the pixel level, making thousands of computations through ascending fields of abstraction – a concept called transfer learning.

Do ImageNet models transfer better?

Our main contributions are as follows: Better ImageNet networks provide better penultimate layer features for transfer learning with linear classi- fication (r = 0.99), and better performance when the entire network is fine-tuned (r = 0.96).

How big is ImageNet?

Clocking in at 150 GB, ImageNet is quite a beast. It holds 1,281,167 images for training and 50,000 images for validation, organised in 1,000 categories.

How do I access Imagenet?

Go to and click on the data tab. You can use the Kaggle API to download on a remote computer, or that page to download all the files you want directly. There, they provide both the labels and the image data.

Who is behind Pytorch?


Original author(s) Adam Paszke Sam Gross Soumith Chintala Gregory Chanan
Initial release September 2016
Stable release 1.9.0 / 15 June 2021
Written in Python C++ CUDA

How many images are in the ImageNet challenge?

It’s also called ImageNet Challenge. For this challenge, the training data is a subset of ImageNet: 1000 synsets, 1.2 million images. Images for validation and test are not part of ImageNet and are taken from Flickr and via image search engines.

What kind of car is the 2010 Camaro?

This review is based mainly on a V-6 Camaro 2LT, which is one of two LT trim levels that fall between the base LS and the V-8 SS. Just to complicate things, our test car also had the RS option package, which includes mostly cosmetic changes. The 2010 Camaro’s styling doesn’t light my fire, but neither did the classic Camaros that inspired it.

What was the error rate of ImageNet before 2012?

Prior to 2012, the image classification model error rate was around 25% but AlexNet shockingly surpassed that error rate with 15.3 in the 2012 ImageNet challenge. AlexNet is often regarded as the pioneer of the convolutional neural network and starting point of the Deep Learning boom.

How many seconds does a Camaro Challenger take?

The Challenger SE with 250 hp takes 7.8 seconds and the lighter Mustang V6 with 210 hp manages only 6.9 seconds. So is it wrong for me to look at the Camaro’s 304 hp and expect even more?