There are 1.8 million train images from 365 scene categories in the Places365-Standard, which are used to train the Places365 CNNs. There are 50 images per category in the validation set and 900 images per category in the testing set.
- Places365 Development kit
Overview and statistics of the data.
meta data for the scene categories.
Matlab routines for evaluation.
Image list of train and val for Places365-Standard and Places365-Challenge
Please be sure to read the included README file for details. The development kit includes
Train images. 105GB. MD5: 67e186b496a84c929568076ed01a8aa1
Validation images. 2.1GB. MD5: 9b71c4993ad89d2d8bcbdc4aef38042f
Test images. 19GB. MD5: 41a4b6b724b1d2cd862fb3871ed59913
The images in the above archives have been resized to have a minimum dimension of 512 while preserving the aspect ratio of the image. Original images that had a dimension smaller than 512 have been left unchanged.
Small images (256 * 256)
Train images. 24GB. MD5: 53ca1c756c3d1e7809517cc47c5561c5
Validation images. 501M. MD5: e27b17d8d44f4af9a78502beb927f808
Test images. 4.4G. MD5: f532f6ad7b582262a2ec8009075e186b
The images in the above archives have been resized to 256 * 256 regardless of the original aspect ratio.
Small images (256 * 256) with easy directory structure
Train and val images. 21G.
These images are 256x256 images, in a more friendly directory structure that in train and val split the images are organized such as train/reception/00003724.jpg and val/raft/000050000.jpg. So you could use pyTorch example script to train network directly as: python main.py -a resnet18 places365_standard.
LMDB data for the 256 * 256 images