README
¶
DLDataset 
Download the ImageNet dataset
The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset has 1000 categories and 1.2 million images. The images do not need to be preprocessed or packaged in any database, but the validation images need to be moved into appropriate subfolders.
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Download the images from http://image-net.org/download-images
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Extract the training data:
mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done
cd ..
- Extract the validation data and move images to subfolders:
mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash
Todo
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ImageNet Validation Dataset
Documentation
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Index ¶
Constants ¶
This section is empty.
Variables ¶
View Source
var ( // Config holds the data read by rai-project/config Config = &dldatasetConfig{ done: make(chan struct{}), } )
Config ...
Functions ¶
Types ¶
type Dataset ¶
type Dataset interface {
New(ctx context.Context) (Dataset, error)
Category() string
Name() string
CanonicalName() string
Download(ctx context.Context) error
List(ctx context.Context) ([]string, error)
Load(ctx context.Context) error
Get(ctx context.Context, name string) (LabeledData, error)
Next(ctx context.Context) (LabeledData, error)
io.Closer
}
Dataset ...
type LabeledData ¶
type LabeledData interface {
Label() string
Feature() *dlframework.Feature
Features() dlframework.Features
Data() (interface{}, error)
}
LabeledData ...
Source Files
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