Contents
What are the 1000 classes in ImageNet?
IMAGENET 1000 Class List
| Class ID | Class Name |
|---|---|
| 1 | goldfish, Carassius auratus |
| 2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon caharias’, |
| 3 | tiger shark, Galeocerdo cuvieri |
| 4 | hammerhead, hammerhead shark |
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.
How many classes are there in ImageNet?
Based on statistics about the dataset recorded on the ImageNet homepage, there are a little more than 14 million images in the dataset, a little more than 21 thousand groups or classes (synsets), and a little more than 1 million images that have bounding box annotations (e.g. boxes around identified objects in the …
Does ImageNet include people?
An assessment of the 1.4 million images included in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset found that 17% of the images contain faces, despite the fact that only three of 1,000 categories in the dataset mention people.
What are the classes of ImageNet?
The Tiny ImageNet dataset has 100,000 images across 200 classes. Each class has 500 training images, 50 validation images, and 50 test images. Thus, the dataset has 10,000 test images. The entire dataset can be downloaded from a Stanford server.
What are the classes in ImageNet?
ImageNet crowdsources its annotation process. Image-level annotations indicate the presence or absence of an object class in an image, such as “there are tigers in this image” or “there are no tigers in this image”. Object-level annotations provide a bounding box around the (visible part of the) indicated object.
What size are ImageNet images?
The average resolution of an ImageNet image is 469×387. They are usually cropped to 256×256 or 224×224 in your image preprocessing step.
Why is ImageNet 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.
How many GB is Mnist?
2.5 GB
Street View House Numbers – A dataset for object detection problems. Similar to MNIST dataset with minimum data pre-processing but more labeled data collected from Google Street viewed house numbers. The size is 2.5 GB.
How do I get ImageNet?
ImageNet Download: Go to https://www.kaggle.com/c/imagenet-object-localization-challenge 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.
What is the use of ImageNet?
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.