landskape-ai / ImageNet-Downsampled

Training code for downsampled ImageNet datasets

Scripts to train ResNets on Downsampled Variants of the ImageNet dataset

Small-ImageNet

A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

Download and extract dataset: python utils/prepare_dataset.py --dataset SmallImageNet --resolution 32 --data-dir data --download-dir data/compressed

Supported resolutions: 8, 16, 32, 64 (must be >=32 for ImageNet ResNets)

Training:

CIFAR ResNets: python train.py data --dataset SmallImageNet --size 32 --classes 1000 --depth 20 --ngpu 1 --epochs 200 -b 128 --lr 0.1 --momentum 0.9 --wd 5e-4 --prefix test --project Imagenet

ImageNet ResNets: python train.py data --dataset SmallImageNet --size 32 --classes 1000 --depth 18 --ngpu 1 --epochs 100 -b 256 --lr 0.1 --momentum 0.9 --wd 1e-4 --prefix test --project Imagenet

classes can be changed to select a subset of the dataset. size is the resolution of the dataset.

Tiny-ImageNet

Tiny ImageNet Visual Recognition Challenge

Download and extract dataset: python utils/prepare_dataset.py --dataset TinyImageNet --data-dir data --download-dir data/compressed

Training:

CIFAR ResNets: python train.py data --dataset TinyImageNet --depth 20 --ngpu 1 --epochs 200 -b 128 --lr 0.1 --momentum 0.9 --wd 5e-4 --prefix test --project Imagenet

ImageNet ResNets: python train.py data --dataset TinyImageNet --depth 18 --ngpu 1 --epochs 100 -b 256 --lr 0.1 --momentum 0.9 --wd 1e-4 --prefix test --project Imagenet

Utils

Run python utils/compute_stats.py --dataset SmallImageNet --resolution 8 --data-dir data to compute mean and std. of the dataset. dataset_stats.json contains stats for 1000 classes.