ImageNet16: Small scale ImageNet Classification

This is a subset of ImageNet called "ImageNet16" more suited for cases with limited computational budget and faster experimentation. 

Each class has 400 train images and 100 test images.

The classes corresponding to imagenet1K:

• n02009912 American_egret

• n02113624 toy_poodle

•  n02123597 Siamese_cat

• n02132136 brown_bear

• n02504458 African_elephant

• n02690373 airliner

• n02835271 bicycle-built-for-two

• n02951358 canoe

• n03041632 cleaver

• n03085013 computer_keyboard

• n03196217 digital_clock

• n03977966 police_van

• n04099969 rocking_chair

• n04111531 rotisserie

• n04285008 sports_car

• n04591713 wine_bottle

 

From original map.txt

knife =    n03041632

keyboard = n03085013

elephant = n02504458

bicycle =  n02835271

airplane = n02690373

clock =    n03196217

oven =     n04111531

chair =    n04099969

bear =     n02132136

boat =     n02951358

cat =      n02123597

bottle =   n04591713

truck =    n03977966

car =      n04285008

bird =     n02009912

dog =      n02113624

 

Folder Structure

-<train>

  -- <class idx 1>

      --- <filename1>.JPEG

      --- <filename2>.JPEG

      --- ....

  -- <class idx 1>

  --...

 

-<val>

  -- <class idx 1>

      --- <filename1>.JPEG

      --- <filename2>.JPEG

      --- ....

  -- <class idx 1>

  --...

 

Some preliminary results:

Model Name	Accuracy (Top-1)
VGG16	85.3
ResNet50	88.2
MobileNetV2	91.0
EfficientNet B0	85.6

* Credit also goes to original creators that constructed the dataset. Unfortunately, I was not able to relocated it online so I reupload it here.

Massive Credit to original ImageNet authors
[1] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei.ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015