What is the size of ImageNet dataset?
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.
Which is the best ImageNet model?
1. Very Deep Convolutional Networks for Large-Scale Image Recognition(VGG-16) The VGG-16 is one of the most popular pre-trained models for image classification. Introduced in the famous ILSVRC 2014 Conference, it was and remains THE model to beat even today.
What are the classes in ImageNet?
IMAGENET 1000 Class List
| Class ID | Class Name |
|---|---|
| 0 | tench, Tinca tinca |
| 1 | goldfish, Carassius auratus |
| 2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon caharias’, |
| 3 | tiger shark, Galeocerdo cuvieri |
Does ImageNet generalize ImageNet?
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. …
What is 21K ImageNet?
ImageNet-21K dataset, which contains more pictures and classes, is used less frequently for pretraining, mainly due to its complexity, and underestimation of its added value compared to standard ImageNet-1K pretraining.
Is ImageNet solved?
Today, many consider ImageNet solved—the error rate is incredibly low at around 2%. But that’s for classification, or identifying which object is in an image.
What is Pretrained dataset?
A pre-trained model is a model that was trained on a large benchmark dataset to solve a problem similar to the one that we want to solve. Accordingly, due to the computational cost of training such models, it is common practice to import and use models from published literature (e.g. VGG, Inception, MobileNet).
What are ImageNet weights?
The Convolutional Neural Network is then instantiated on Line 59 using the pre-trained ImageNet weights; Note: Weights for VGG16 and VGG19 are > 500MB. ResNet weights are ~100MB, while Inception and Xception weights are between 90-100MB.
Who created ImageNet?
Dr. Fei-Fei Li
Dr. Fei-Fei Li is the inventor of ImageNet and the ImageNet Challenge, a critical large-scale dataset and benchmarking effort that has contributed to the latest developments in deep learning and AI.
What is ObjectNet?
ObjectNet, a dataset of photos created by MIT and IBM researchers, shows objects from odd angles, in multiple orientations, and against varied backgrounds to better represent the complexity of 3D objects. The researchers hope the dataset will lead to new computer vision techniques that perform better in real life.
What is JFT 300M?
JFT-300M is an internal Google dataset used for training image classification models. Images are labeled using an algorithm that uses complex mixture of raw web signals, connections between web-pages and user feedback.
How do you cite an ImageNet?
Citation in Harvard style Deng, J. et al., 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition.
How many images are in the ImageNet dataset?
The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.
Are there 1000 synsets in the 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. There are 50K images for validation and 150K images for testing. These are hand-labeled with the presence or absence of 1000 synsets.
How does ImageNet share images with the public?
ImageNet doesn’t own the copyright for any of the images. This has implication on how ImageNet shares the images to researchers. For public access, ImageNet provides image thumbnails and URLs from where the original images were downloaded. Researchers can use these URLs to download the original images.
Where did the idea for ImageNet come from?
The idea of the ImageNet visual database was conceived by Fei-Fei Li, a Professor of Computer Science at Stanford University in 2006. In those days, she was working in the field of medical imaging and faced issues in designing machine learning models due to a lack of quality images.