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. * Credit also goes to original creators that constructed the dataset. Unfortunately, I was not able to relocated it online so I reupload it here. If used in your work please cite as follows: C. Kyrkou, "Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2024.3380827. 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 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